Gene Regulatory Network Inference from Expression and Perturbation Data¶
- Task ID:
biology.gene_network_timeseries - Domain:
biology - Subdomain:
systems_biology - Status:
test - Tags:
gene_regulatory_network,network_inference,causal_inference,knockout,perturbation,systems_biology
Public Summary¶
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Example B1 Prompt Excerpt¶
# Gene Regulatory Network Inference from Expression and Perturbation Data
## Background
You are given data from a gene regulatory network (GRN) consisting of {{ n_genes }} genes. The underlying dynamics follow a nonlinear ODE model with Hill-function regulation:
$$\frac{dx_i}{dt} = b_i + \sum_{j: W_{ij}>0} W_{ij} \cdot \frac{x_j^n}{K^n + x_j^n} + \sum_{j: W_{ij}<0} |W_{ij}| \cdot \frac{K^n}{K^n + x_j^n} - \gamma_i \cdot x_i$$
where $K = 0.5$ (half-activation constant) and $n = 2$ (Hill coefficient).
Two types of data are provided:
1. **Wild-type time series**: {{ n_series }} independent time courses of {{ n_timepoints }} time points, sampled every {{ dt }} time units.
2. **Multi-gene perturbation steady states**: {{ n_perturbations }} perturbation experiments, each disrupting **multiple genes simultaneously** through different mechanisms. Row 0 of the perturbation data is the wild-type steady state. The remaining rows correspond to multi-gene interventions.
Notes¶
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