Cytoplasm Subsystem

The "cytoplasm" subsystem is responsible for implementing and maintaining the internal environment of the synthetic cell, including key mechanisms such as transcription, translation, and degradation. There are many different approaches to implementing the cytoplasm subsystem, depending on the desired functionality of the synthetic cell. On this page we describe some of the key functions and approaches to implementing those functions.

Transcription and Translation

The functions of transcription and translation enable to expression of RNA and proteins from DNA. They are often key elements in synthetic cells since they allow genetic programs that can conditionally express molecules with prescribed functionality.

Transcription only systems

Systems that implement just transcription, such as genelets.

Purified transcription and translation systems (PURE)

PURExpress, PUREFrex, OnePot pure

Degradation and Waste Management

Another function of the cytoplasm subsystem is to help degrade products of transcription and translation when they are no longer needed and to manage any waste products that are produced by the cytoplasm. These functions often make use of other subsystems, such as the Transport Subsystem.

Non-Transcription/Translation-Based Systems

In some cases, a synthetic cell may not make use of transcription nor translation as its mechanism for implementing the functionality of synthetic cells.

Implementation in BioCRNpyler

BioCRNpyler contains a number of modules and mechanisms that can be used to model the cytoplasm subsystem.

The simplest implementation of transcription and translation makes use of the pre-defined `TxTlExtract` mixt

 import numpy as np
 import matplotlib.pyplot as plt
 import biocrnpyler as bcp
 
 # Create a simple GFP DNA construct
 gfp_DNA = bcp.DNAassembly('GFP', promoter='pconst', rbs='rbs')
 
 # Create a TXTL mixture containing the GFP
 mixture = bcp.TxTlExtract(
     "TX-TL of GFP", components=[gfp_DNA], 
     parameters={'ktx': 0.5, 'ktl': 2, 'kb': 100, 'ku':10, 'kdeg': 100})
 
 # Compile the CRN and generate a simulation
 gfp_CRN = mixture.compile_crn()
 
 timepts = np.linspace(0, 100)
 gfp_sim = gfp_CRN.simulate_with_bioscrape_via_sbml(
     timepts, initial_condition_dict={
         'dna_GFP': 1, 'protein_RNAP': 10, 'protein_Ribo': 50})
 
 plt.plot(timepts, gfp_sim['protein_GFP'])
 plt.xlabel("Time, min")  # default units = minutes
 plt.ylabel("[GFP], nM")  # default units = nM

yielding the plot