Bio-SoS is a package for simulating iPSC cultures under different conditions.
Large-scale manufacturing of induced pluripotent stem cells (iPSCs) is essential for cell therapies and regenerative medicines. Yet, the self-aggregation iPSCs can generate large cell aggregates in suspension bioreactors, resulting in insufficient nutrient supply and extra metabolic waste build-up for the cells located at core. Since subtle changes in micro-environment can lead to cell stress and heterogeneous cell population, a novel Biological System-of-Systems (Bio-SoS) framework is proposed to characterize cell-to-cell interactions, spatial heterogeneity, and cell response to micro-environmental variation. Building on iPSC stochastic metabolic reaction network, aggregation kinetics, and reaction-diffusion mechanisms, the Bio-SoS model can quantify the impact of factors (i.e., aggregate size) on cell product health and quality heterogeneity, accounting for causal interdependencies at individual cell, aggregate, and cell population levels. The proposed framework can accurately predict iPSC culture conditions for both monolayer and aggregate cultures, where these predictions can be leveraged to ensure that the culture process follows an expected trajectory for successful cell growth and expansion.
Zheng, H., Harcum, S.W., Pei, J. et al. Stochastic biological system-of-systems modelling for iPSC culture. Commun Biol 7, 39 (2024). https://doi.org/10.1038/s42003-023-05653-w
To create a virtual environment, decide upon a directory where you want to place it, and run the venv module as a script with the directory path:
python3 -m venv venv
source venv/bin/activateUse pip to install all required dependencies
pip install -r requirements.txtthe main.py script provides useful help messages that describe the available command-line arguments and their usage.
python main.py --helpRead the help messages to check out hyperparamters:
usage: main.py [-h] [-val VALIDATION_METABOLITE_CONSUMPTION] [-ss STEADY_STATE_CONCENTRATION_PROFILES] [-f FLUX_RATES_AND_METABOLITE_CONC] [-c COMPARISON_UNDER_MONOLAYER_CONDITION] [-v VARIANCE_COMPONENT_ANALYSIS] [-m INNER_OUTER_CELL_METABOLISM] [-ch HEALTH_CONDITION]
[-size GET_BIOMASS_YIELD_VARIANCE_ANALYSIS] [-pbm PBM] [-r SINGLE_CELL_RADIUS] [-t TORTUOSITY] [-dt DELTA_T] [-p POROSITY]
optional arguments:
-h, --help show this help message and exit
-val VALIDATION_METABOLITE_CONSUMPTION, --validation-metabolite-consumption VALIDATION_METABOLITE_CONSUMPTION
validation metabolite consumption.
-ss STEADY_STATE_CONCENTRATION_PROFILES, --steady-state-concentration-profiles STEADY_STATE_CONCENTRATION_PROFILES
Biomass yield and variance analysis.
-pbm PBM, --population-balance-model PBM
Run population balance model. PBM takes long time (about 12 hours).
-r SINGLE_CELL_RADIUS, --single-cell-radius SINGLE_CELL_RADIUS
Set single cell raidus.
-t TORTUOSITY, --tortuosity TORTUOSITY
Set tortuosity.
-dt DELTA_T, --delta-t DELTA_T
Set simulation time interval (h)
-p POROSITY, --porosity POROSITY
Set porosityimport pickle
from multi_scale_model.aggregate import Aggregate, get_aggregation_profile
from multi_scale_model.Util import *
# HGLL
# simulation parameters
test_index = 1
porosity = 0.27 # 0.27 at 60 rpm, and 0.229 at 100 rpm of agitation rate
cell_growth_rate = 0.043
single_cell_radius = 7.5
hours = 48
bioreactor = True
# Load PBM model for aggregation size distribution and configure the culture setting (monolayer versus bioreactor)
file = open("multi_scale_model/data/result_all.pickle", 'rb')
parameters = pickle.load(file)
parameters = parameters.params
if bioreactor:
agg_density_function = get_aggregation_profile(no_aggregate=False, bioreactor=True) # bioreactor
else:
agg_density_function = get_aggregation_profile(no_aggregate=True, bioreactor=False) # monolayer
# Load initial metabolite concentrations of iPSC cell culture
data_whole, mean_whole, std_whole = get_data('dynamic_model/iPSC_data.xlsx')
x0_LGHL = np.hstack([S2_0 * 1000 * mean_whole[test_index][0, -1], mean_whole[test_index][0]])
agg_simulator = Aggregate(cell_growth_rate, agg_density_function, meta_index, x0_LGHL,
single_cell_radius, parameters, diffusion_coef, porosity, delta_t=1)
for i in range(hours):
agg_simulator.simulate()
print('time', (i + 1) * 1, 'GLC', agg_simulator.extra_metabolites[meta_label_index['GLC'][1]],
'Lac', agg_simulator.extra_metabolites[meta_label_index['ELAC'][1]])The solution object is the same as class attribute results. We can plot the glucose concentration trajectory using matplotlib:
import numpy as np
import matplotlib.pyplot as plt
from multi_scale_model.Util import meta_label_index
plt.plot(np.array(agg_simulator.results)[:hours, meta_label_index['GLC'][0]]) # plot simulated trajectory of glucose
plt.show()
Lactate concentration trajectory:
import numpy as np
import matplotlib.pyplot as plt
from multi_scale_model.Util import meta_label_index
plt.plot(np.array(agg_simulator.results)[:hours, meta_label_index['ELAC'][0]]) # plot simulated trajectory of lactate
plt.show()
1(a). Generate temporal size distribution of aggregation population balance model (PBM) by using pretrained model
(-pretrained_pbm True). (Fig. 2a and Fig. 2b)
python main.py -pbm True -pretrained_pbm True1(b). Generate temporal size distribution of aggregation population balance model (PBM) without using pretrained model
(-pretrained_pbm False). The parameter estimation can take more than 12 hours.(Fig. 2a and Fig. 2b)
python main.py -pbm True -pretrained_pbm True
2. Perform Biomass yield and variance analysis (Fig. 8).
python main.py -size True
3. Analyze iPS cell health condition (Fig. 5).
python main.py -ch True
4. Generate the figure for metabolic heterogeneity of aggregates of varying sizes (Fig. 7).
python main.py -m True
5. Comparison between predicted metabolite concentrations from a single-cell mechanistic model and a multi-scale model with experimentally measured bulk metabolite concentrations. (Fig. 3)
python main.py -c True
6. Metabolic heterogeneity. Reaction flux rates and extracellular metabolite concentrations with various aggregates ranging from 30 μm to 600 μm in radius. (Fig. 6)
python main.py -f True
7. Generate steady state concentration profiles at 48 hours (Fig. 2c).
python main.py -ss True
- validation metabolite consumption in a bioreactor culture of iPSCs (Fig. 4).
python main.py -val True
If you have any questions, or just want to chat about using the package, please feel free to contact me in the Website. For bug reports, feature requests, etc., please submit an issue to the email zheng.hua1@northeastern.edu.