Taxa knockouts¶
An interesting question concerning any given microbial community is how dependent the growth of one bacteria is one the presence of other bacteria. One strategy to answer that is to perform in silico knockouts for each individual taxa in a community and observe how this changes the growth rates of other bacteria. If the knowckout of one bacteria increases the growth of another one this indicates competitivity whereas a dimished growth rate indicted cooperativity. In order to get unique growth rate solutions we will again use cooperative tradeoff.
For large models as managed in micom running those knockouts can be
challenging due to numerical problems and the tendency of the solvers to
get stuck in a previous solutions. micom thus implements an
optimized knockout algorithms that avoids numerical and solver issues as
much as possible.
Still you should be aware that knockouts are computationally instensive due to the large numbers of quadratic and linear programming problems that have to be solved. For realistic communities with 10-500 taxa you should expect a full knockout experiment to take between 1 and 5 hours. Here we will perform the knockouts for our small E. coli example.
In [1]:
from micom import Community, data
tax = data.test_taxonomy()
com = Community(tax, solver="gurobi")
ko = com.knockout_species(fraction=1.0)
ko
100%|██████████| 5/5 [00:00<00:00, 5.91models/s]
100%|██████████| 5/5 [00:00<00:00, 23.65knockout(s)/s]
Out[1]:
| compartments | Escherichia_coli_1 | Escherichia_coli_2 | Escherichia_coli_3 | Escherichia_coli_4 | Escherichia_coli_5 |
|---|---|---|---|---|---|
| Escherichia_coli_1 | -0.873922 | 0.229162 | 0.229162 | 0.229162 | 0.229162 |
| Escherichia_coli_2 | 0.229162 | -0.873922 | 0.229162 | 0.229162 | 0.229162 |
| Escherichia_coli_3 | 0.229162 | 0.229162 | -0.873922 | 0.229162 | 0.229162 |
| Escherichia_coli_4 | 0.229162 | 0.229162 | 0.229162 | -0.873922 | 0.229162 |
| Escherichia_coli_5 | 0.229162 | 0.229162 | 0.229162 | 0.229162 | -0.873922 |
By default knockout_species returns the changes in growth rate
meaning the difference between the growth rate after the knockout and
the growth rate before. Rows denote the taxa that has been kncked out
and columns the changes in growth rate. As we can see all knockouts
increase the growth rates of the remaining E. coli strains in this
example which makes sense since all of them compete for the same
resources. Alternatively we can also just get the new growth rates after
the knockout
In [2]:
com.knockout_species(fraction=1.0, method="raw")
100%|██████████| 5/5 [00:00<00:00, 23.00knockout(s)/s]
Out[2]:
| compartments | Escherichia_coli_1 | Escherichia_coli_2 | Escherichia_coli_3 | Escherichia_coli_4 | Escherichia_coli_5 |
|---|---|---|---|---|---|
| Escherichia_coli_1 | 0.000000 | 1.103083 | 1.103083 | 1.103083 | 1.103083 |
| Escherichia_coli_2 | 1.103083 | 0.000000 | 1.103083 | 1.103083 | 1.103083 |
| Escherichia_coli_3 | 1.103083 | 1.103083 | 0.000000 | 1.103083 | 1.103083 |
| Escherichia_coli_4 | 1.103083 | 1.103083 | 1.103083 | 0.000000 | 1.103083 |
| Escherichia_coli_5 | 1.103083 | 1.103083 | 1.103083 | 1.103083 | 0.000000 |
…or the relative change ((new - old)/old):
In [3]:
com.knockout_species(fraction=1.0, method="relative change")
100%|██████████| 5/5 [00:00<00:00, 23.95knockout(s)/s]
Out[3]:
| compartments | Escherichia_coli_1 | Escherichia_coli_2 | Escherichia_coli_3 | Escherichia_coli_4 | Escherichia_coli_5 |
|---|---|---|---|---|---|
| Escherichia_coli_1 | -1.000000 | 0.262222 | 0.262222 | 0.262222 | 0.262222 |
| Escherichia_coli_2 | 0.262222 | -1.000000 | 0.262222 | 0.262222 | 0.262222 |
| Escherichia_coli_3 | 0.262222 | 0.262222 | -1.000000 | 0.262222 | 0.262222 |
| Escherichia_coli_4 | 0.262222 | 0.262222 | 0.262222 | -1.000000 | 0.262222 |
| Escherichia_coli_5 | 0.262222 | 0.262222 | 0.262222 | 0.262222 | -1.000000 |
In certain circumstances the diagonal entries can be confusing since the
knocked out taxa always loses its entire growth. We can suppress the
diagonal using the diag argument.
In [4]:
com.knockout_species(diag=False)
100%|██████████| 5/5 [00:00<00:00, 21.50knockout(s)/s]
Out[4]:
| compartments | Escherichia_coli_1 | Escherichia_coli_2 | Escherichia_coli_3 | Escherichia_coli_4 | Escherichia_coli_5 |
|---|---|---|---|---|---|
| Escherichia_coli_1 | NaN | 0.229162 | 0.229162 | 0.229162 | 0.229162 |
| Escherichia_coli_2 | 0.229162 | NaN | 0.229162 | 0.229162 | 0.229162 |
| Escherichia_coli_3 | 0.229162 | 0.229162 | NaN | 0.229162 | 0.229162 |
| Escherichia_coli_4 | 0.229162 | 0.229162 | 0.229162 | NaN | 0.229162 |
| Escherichia_coli_5 | 0.229162 | 0.229162 | 0.229162 | 0.229162 | NaN |