A basic knowledge of Python (and, optionally, of Jupyter Notebooks) is all that is required to run "in-silico experiments" (simulations) with Life123 !
NOTE: for examples of usage, see the Experiments. The unit tests of the various python functions can also be of help. For the JavaScript modules, see the Visualization section.
How to use these libraries: see our quick start page!
1D simulations of diffusion and reactions, with an early partial implementation of membranes.
2D simulations of diffusion and reactions
3D simulations of diffusion and reactions
Data about all the chemicals and (if applicable) reactions,
including:
- names
- diffusion rates
- macro-molecules Binding Site Affinities (for Transcription Factors)
Notes: * for now, the temperature is assumed constant everywhere, and unvarying (or very slowly varying)
* this class contains (extends) the following other classes: ChemCore, Diffusion, Macromolecules
For the management of reaction diagnostic data
Assorted, general numerical methods
Data about a SINGLE reaction,
including:
- stoichiometry
- kinetic data (reaction rates, reaction orders)
- thermodynamic data (temperature, changes in enthalpy/entropy/Gibbs Free Energy)
- list of involved enzymes
(Note: this data will eventually be stored in a Neo4j graph database)
Each reaction contains:
"reactants"
"products"
"kF" (forward reaction rate constant)
"kR" (reverse reaction rate constant)
"K" (equilibrium constant - from either kinetic or thermodynamic data; if both present, they must match up!)
"Delta_H" (change in Enthalpy: Enthalpy of Products - Enthalpy of Reactants)
"Delta_S" (change in Entropy)
"Delta_G" (change in Gibbs Free Energy)
Note - at constant temperature T : Delta_G = Delta_H - T * Delta_S
Equilibrium constant = exp(-Delta_G / RT)
"enzymes" (list of the indices of the chemical species that appear as catalysts in the reaction)
Each Reactant and each Product is a triplet of the form: (stoichiometry, species index, reaction order).
The "reaction order" refers to the forward reaction for reactants, and the reverse reaction for products.
Data about a SINGLE enzyme-catalyzed reaction that can be modeled kinetically as: E + S <-> ES <-> E + P
Manage reaction-related data
2 classes: Static methods about reactions kinetics / Methods for managing variable time steps during reactions
Manage the Thermodynamics aspects of reactions:
changes in Gibbs Free Energy, Enthalpy, Entropy - and how
they relate to equilibrium constant, at a given temperature.
This class does NOT get instantiated
"K" (equilibrium constant - from either kinetic or thermodynamic data;
if both present, they must match up!)
"delta_H" (change in Enthalpy: Enthalpy of Products - Enthalpy of Reactants)
"delta_S" (change in Entropy)
"delta_G" (change in Gibbs Free Energy)
Note - at constant temperature T :
Delta_G = Delta_H - T * Delta_S
Equilibrium constant = exp(-Delta_G / RT)
Used to simulate the dynamics of reactions (in a single compartment)
To simplify use of HtmlLog and Vue components from within this project
To assist in the use of the plotly library
Facilitate data preparation for graph visualization using the Cytoscape.js library. The development of this library is shared with the sister project BrainAnnex.org
An HTML logger to file, plus optional plain-text printing to standard output
A "tabular collection" is a Pandas dataframe
built up from a sequence of "snapshots" of data that's in the form of a python dictionary
(representing a list of values and their corresponding names),
such as the state of the system or of parts thereof.
Each data "snapshots" is taken at different times,
or results from varying some parameter.
Each snapshot - incl. its parameter values and optional captions -
will constitute a "row" in a tabular format
MAIN DATA STRUCTURE for "tabular" collections:
A Pandas dataframe
Use this structure if your "snapshots" (data to add to the cumulative collection) are Numpy arrays,
of any dimension - but always retaining that same dimension.
Usually, the snapshots will be dump of the entire system state, or parts thereof, but could be anything.
Typically, each snapshot is taken at a different time (for example, to create a history), but could also
be the result of varying some parameter(s)
DATA STRUCTURE:
A Numpy array of 1 dimension larger than that of the snapshots
EXAMPLE: if the snapshots are the 1-d numpy arrays [1., 2., 3.] and [10., 20., 30.]
then the internal structure will be the matrix
[[1., 2., 3.],
[10., 20., 30.]]
A "Collection" is a list of snapshots of any values that the user wants to preserve,
such as the state of the entire system, or of parts thereof,
either taken at different times,
or resulting from varying some parameter(s)
This class accept data in arbitrary formats
MAIN DATA STRUCTURE:
A list of triplets.
Each triplet is of the form (parameter value, caption, snapshot_data)
1) The "parameter" is typically time, but could be anything.
(a descriptive meaning of this parameter is stored in the object attribute "parameter_name")
2) "snapshot_data" can be anything of interest, typically a clone of some data element.
3) "caption" is just a string with an optional label.
If the "parameter" is time, it's assumed to be in increasing order
EXAMPLE:
[
(0., DATA_STRUCTURE_1, "Initial state"),
(8., DATA_STRUCTURE_2, "State immediately after injection of 2nd reagent")
]