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Stochastic IP3R Channel Dynamics and Calcium Oscillations

  • Task ID: biology.calcium_ip3_dyk_oscillations
  • Domain: biology
  • Subdomain: systems_biology
  • Status: final
  • Benchmark set: seed42 (60 tasks)
  • Tags: calcium_signaling, ip3r, stochastic_simulation, gillespie, coherence_resonance, cellular_signaling

Runtime and requirements

  • Estimated time: 30-60 minutes
  • Python: >=3.11
  • Packages: numpy>=2.0, scipy>=1.11, matplotlib>=3.7
  • GPU required: no
  • Network required: no

Public input and output contract

Inputs

  • system_info.json (data): Public receptor state model, binding-interaction constants, compartment volumes, pump/leak parameters, and observation window settings
  • scan_spec.json (data): IP3 concentrations (ip3_levels) and channel counts (channel_counts) to sweep, T_obs, dt_out, n_replicates
  • example_trajectory.npy (data): One example [Ca2+]_cytosol trace at a mid-range IP3 and mid-range channel count, for sampling-format sanity check

Outputs

  • simulate.py (code): Simulation script implementing the IP3R channel dynamics + calcium balance
  • ca_traces.npz (data): Calcium traces for all (ip3_idx, N_idx) combinations; keys format 'ip3{i}_N{j}' with shape [n_replicates, n_samples]
  • summary_stats.json (data): period_vs_ip3 (seconds), cv_isi_vs_N, p_open_vs_ip3, conservation_error_max, cr_ip3_idx_used
  • calcium_summary.png (image): Multi-panel diagnostic figure combining representative Ca traces, period-vs-IP3, CV-vs-N, and P_open-vs-IP3.

Public repository files

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Provenance

This task is included in the current seed42 benchmark set at dataset revision f11a199f71fb4b854ac43a1bf548d5df519141a9.

This page is generated only from files tracked in the public ASI-Bench repository at commit f18382f03faf. Submission bundles, run logs, private scoring configuration, and private reference answers are not read by this page generator.