<jupyter_start><jupyter_text>Overview of implementing omega on real experimental dataset Steps1. Determine biochemical process that generated the experimental data and program this into Omega    1. An example of this is a Gillespie simulation (although it doesn't need to be)2. This implementation can run simulations which follow the same biochemical laws as the experiment    1. For a given set of cells $X$ we can generate traces over some time period $T$     2. For each time point $t_i$ in $T$ we can describe the cell abundance as $P(x_1, x_2, .., x_n|t_i)$3. We can condition the implementation on our experimental data to recreate the experimental state    $$\sum\nolimits_{t_i \in T} P(x_1, x_2,.., x_n|\text{Data}, t_i)$$4. Use Omega's functionality (ie replace) on the conditioned trace to ask counterfactual queries about the experiment  Biochemical model of organelle dynamicsQuick reminder on what our experimental data looks like.<jupyter_code>import pandas as pd
day1 = pd.read_csv('../../../../../Research/Causal_Inference/SDE_inference/Experimental_Data/Data/Day1/all.dat', sep=',').iloc[:, 1:]
day1.head()<jupyter_output><empty_output>
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