silvio 0.2.3

silvio is an environment for Simulation of Virtual Organisms. silvio contains several linked microbial models.

Silvio

silvio is an environment to combine microbiological models to simulate virtual cells.

• Authors: Ulf Liebal, Lars Blank

• Contact: [e-mail]

• Licence: MIT Licence

• Free software: MIT license

• Documentation: https://silvio.readthedocs.io

Features

• Simulate different components of a biological host using event-driven messaging.

• Produce data on procedures performed on the host at the given state.

Writing a Module

Prefer the use of [standardized code style](https://pep8.org/).

Make use of [python type hints](https://docs.python.org/3/library/typing.html) whenever possible. When specifying types for variables and methods, your IDE will help you with organizing the inputs, outputs and arguments that you may use.

# Initial definition of a variable to store a probability
some_probability: float = 0

some_probability = 0.4      # Will work. The variable may receive fractional numbers.
some_probability = 0        # Will work. Integers are also numbers.

some_probability = "a lot"  # Error! The IDE will notify us about this bad assignment.
some_probability = "0.3"    # Error! This is still a string. No more conversion problems.

some_probability = -1.4     # Unfortunately this still works. Typing only defines simple types.

When writing classes, keep all properties (variables inside a class) at the top of the class definition, outside of the constructor. The constructor should only perform the initial assignment.

class BayesianNetworkNode :
    """
    Each class should document what it does. Ideally, it should have a single purpose.
    """

    # Probability that this node is true.
    true_prob: float

    # Probability that the node is false. Should be inverse of true probability.
    false_prob: float


    def __init__ ( self, true_prob: float ) :

        # Notice that constructor arguments may have the same name as properties.
        self.true_prob = true_prob

        # The constructor only uses necessary arguments to initialize all properties.
        self.false_prob = 1 - true_prob

How to name things is a very debated topic in many languages. When in doubt, follow the conventions that have been laid by the [python standard](https://www.python.org/dev/peps/pep-0008/#naming-conventions). Some common examples are.

# Use lower_case with underscores. Prefer distinct names to single letters.
num_strands = 2

# Constants are values embedded into the code. Use UPPER_CASE with underscores.
GOLDEN_RATIO = 1.6180


# Module names use lower_case and avoids underscore when possible.
import biolabsim.sequencing.evaluation


# Custom types use PascalCase.
from typing import Tuple, Literal
GeneBase = Literal['A','T','C','G']


# Functions use lower_case and typically start with a verb.
def complement_base ( base:GeneBase ) -> GeneBase :  # (input) -> output

    # Include most initilization on top of the method.
    orig_bases: List[GeneBase] = ['A','T','C','G']  # Common words may be shortened. orig = original
    comp_bases: List[GeneBase] = ['T','A','G','C']  # But spell it out in comments.  comp = complementary

    # Split your code into blocks of related operations. Provide a small summary of each block.
    # Comments should help outsiders to skim through the code and to explain programming decisions.
    found_orig_index = orig_bases.index(base)  # Avoid one-liners. Variable names provide context.
    return comp_bases[found_orig_index]


# Use simple types to construct more complex ones.
Codon = Tuple[ GeneBase, GeneBase, GeneBase ]


# Classes use PascalCase as well.
class AminoAcid :

    # Class properties use lower_case as well.
    gene_triplet : Codon


    # Constructors initialize the properties.
    def __init__ ( self, base1:GeneBase, base2:GeneBase, base3:GeneBase ) :
        self.gene_triplet = ( base1, base2, base3 )


    # Leave enough space between method definitions.
    def complement_triplet (self) -> Codon :
        return (                                       # Use multiple lines and more spacing if the
            complement_base( self.gene_triplet[0] ),   # code becomes too bulky.
            complement_base( self.gene_triplet[1] ),
            complement_base( self.gene_triplet[2] ),
        )

Generate Sphinx documentation.

Sphinx is not very automatic on how documentation is extracted from the code. We use [sphinx-apidoc](https://www.sphinx-doc.org/en/master/man/sphinx-apidoc.html) to periodically generate the documentation .rst files.

# Assuming you start at the project root directory.

# Enter the documentation directory.
cd docs

# Remove the old API documentation.
rm -ri ./api

# Generate the new reStructuredText files for the API documentation.
sphinx-apidoc --module-first -d 4 -o api ../biolabsim

# Generate the HTML from all documentation files.
make html

Credits

Extensive credits can be found in the author notes.

History

0.1.0 (2021-10-17)

• First release on PyPI.

0.1.4 (2022-04-07)

• add catalog with RecExpSim functions in src

0.1.5 (2022-04-07)

• add __init__.py to catalog folder

0.1.6 (2022-04-07)

• in RecExperiment: round print failure rate to two decimals

• in RecExperiment.simulate_growth: separate argument progress bar waiting

0.1.7 (2022-05-03)

• remove requirement cobra

0.1.8 (2022-05-03)

• remove cobra code dependencies

0.1.8 (2022-05-03)

• add cobra code dependencies

• remove undelete_gene

0.2.0 (2023-03-29)

• add GroExpSim, a class to simulate growth experiments

0.2.1 (2023-08-20)

• add storage of simulated data to Data folder

0.2.2 (2023-09-02)

• GroExpSim with:

  • measure_DryWeight: measure the OD to DW conversion factor

  • measure_TemperatureGrowth: measure the growth curve at different temperatures

  • measure_BiomassSubstrateExp: measure the growth curve and substrate concentrations

  • check_Results: check the results of the parameters

0.2.2 (2023-09-02)

• GroExpSim, nightshift must be within 15h of experiment