Build a Multi-Agent AI Workflow for Biological Network Modeling, Protein Interactions, Metabolism, and Cell Signaling Simulation

class CellSignalingSimulationAgent: def run(self, df_signal: pd.DataFrame) -> AgentResult: peak_receptor = float(df_signal[“receptor_active”].max()) peak_kinase = float(df_signal[“kinase_active”].max()) peak_tf = float(df_signal[“tf_active”].max()) t_receptor = float(df_signal.loc[df_signal[“receptor_active”].idxmax(), “time”]) t_kinase = float(df_signal.loc[df_signal[“kinase_active”].idxmax(), “time”]) t_tf = float(df_signal.loc[df_signal[“tf_active”].idxmax(), “time”]) final_state = df_signal.iloc[-1].to_dict() summary = { “peak_receptor_activity”: round(peak_receptor, 4), “peak_kinase_activity”: round(peak_kinase, 4), “peak_tf_activity”: round(peak_tf, 4), “time_to_peak_receptor”: round(t_receptor, 4), “time_to_peak_kinase”: round(t_kinase, 4), “time_to_peak_tf”: round(t_tf, 4), “final_state”: {k:…

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