Niederer2006 Myocyte Relaxation Lab

This lab asks: How quickly does the myocyte relax after calcium activation?

The lab is composed of two sub-models: a core SBML simulator (encoded as SBML, solved by Tellurium) and a dedicated viz presenter that turns the raw simulation state into a friendly timeseries plot and a plain-language "What Happened" summary. The split keeps the SBML wrapper clean and makes it easy to swap or extend the visualization without touching the simulator. This a model from the article: A quantitative analysis of cardiac myocyte relaxation: a simulation study. It can be used to explore cardiac dynamics and compare response patterns across conditions.

What You'll See

The lab opens as a canvas with three model nodes wired in series: the scenario driver on the left, the core simulator in the middle, and the visualization sub-model on the right. After running, the viz node emits four visualizations: an event-annotated absolute timeseries, a baseline-relative response timeseries, a signed response bar chart, and a question-and-answer table titled What Happened that answers How quickly does the myocyte relax after calcium activation? in plain language. The viz node also publishes the table as a structured run_summary record that downstream nodes can consume.

Primary variables shown: the core publishes a curated state record for the visualization, and the viz node presents these concepts with user-friendly labels:

• Cytosolic calcium
• Bound calcium
• Troponin calcium-binding pool
• Z

The captured run applies the configured troponin pool challenge, changing troponin pool from 0.067593139865 to 0.101389709798 during the challenge window before returning to baseline. It evaluates 1 s of simulated dynamics across Cytosolic calcium, Bound calcium, Troponin calcium-binding pool, Z; read the absolute trajectories, baseline-relative plot, response ranking, and summary table together because the variables use different native scales.

Output Visualizations

The first chart shows the absolute model state trajectories in their native units with the challenge timing included.

The second chart normalizes each trajectory against its baseline so smaller relative shifts are visible beside larger state variables.

The response ranking summarizes final-minus-baseline changes and highlights which selected variables moved most during the configured run.

The summary table restates the lab question, challenge, simulated duration, strongest response, and interpretation limits in plain language.

How the Models Connect

The canvas has two steps:

1. niederer2006_myocyte_relaxation_core: SBML simulator. Receives every contextual input port (ion concentrations, voltages, conductances, etc. — see below), drives roadrunner, and emits two outputs: state (per-window species/state-variable record) and species_labels (a one-time map of {species_id: human_label} lifted from the SBML <species name=...> attributes).
2. niederer2006_myocyte_relaxation_viz: visualization sub-model. Consumes both state and species_labels from the core, accumulates the per-window history, prettifies the labels, and renders the two visuals plus the run_summary output. No SBML, no roadrunner — pure presentation.

How to Read the Visualizations

The absolute timeseries plot has one curve per state variable in the SBML model and marks when the challenge and recovery windows begin. The baseline-relative plot rescales each curve against the pre-challenge baseline so small but real responses are visible. The x-axis is simulation time in seconds and the y-axis is the variable's value in its native SBML unit (concentrations in mM or mol, voltages in mV, currents in pA, volumes in mL — refer to the SBML file for the exact unit per variable). Look for periodic patterns (action-potential-style depolarisations, oscillations), monotonic trends, or steady-state plateaus.

The response bar chart ranks final-minus-baseline changes in native SBML units. The "What Happened" table explains the lab question, the applied challenge, the simulated duration, the strongest response, and the interpretation limits.

What This Lab Contains

• lab.yaml declares the two sub-models, runtime, IO, and wiring.
• wiring-layout.json places the two nodes on the canvas with the connecting edges.
• models/core/model.yaml describes the SBML simulator package.
• models/core/src/niederer2006_cardiacmyocyterelaxation_model8687196544_model.py is the SBML wrapper (no visualization code).
• models/core/data/MODEL8687196544.xml is the original SBML file from BioModels.
• models/core/tests/ checks instantiation, output accumulation, and output keys.
• models/viz/model.yaml describes the visualization sub-model.
• models/viz/src/niederer2006_myocyte_relaxation_viz.py consumes core state + labels and renders timeseries, Q&A table, and the run_summary record.
• models/viz/tests/ exercises the viz with synthetic state inputs (no SBML or roadrunner needed).

Inputs

This lab exposes a set of contextual scalar ports specific to its SBML, plus three generic fallback ports for everything else.

Contextual ports (recommended)

These map a human-friendly port name onto a real SBML global parameter. Wire them from upstream nodes or set them in lab.yaml runtime.initial_inputs. Each scalar override is applied to roadrunner before the next advance_window call.

Input	Meaning	Default	Unit
troponin_pool	Sets troponin pool for the scenario.	0.067593139865	—
relaxation_rate	Sets relaxation rate for the scenario.	0.002	—
cooperativity	Sets cooperativity for the scenario.	0.008	—

Generic fallback ports

• integration_step (s, scalar): override the ODE solver step. Smaller is more precise but slower. Default 0.001.
• parameter_overrides (record, dict of {parameter_id: value}): apply override values to any SBML global parameter listed in the SBML Parameters table below — useful when you need to override a parameter that has no contextual port. Applied before each window.
• initial_conditions (record, dict of {species_id: value}): override the starting concentration of any species in the SBML Species table below. Applied at setup and on reset.

Outputs

• scenario_metadata (from scenario): scenario name, active challenge input, baseline/challenge/recovery timing, and event markers used by the visualizations.
• state (from core): a record of every species and state variable in the SBML model at each communication step. Units are mixed; see the SBML file for per-species units.
• run_summary (from viz): structured Q&A record echoing the rows in the "What Happened" table — {duration_s, point_count, state_variable_count, rows}. Useful for downstream nodes that want to consume the same plain-language summary the user sees in the visualization.

Running in Biosimulant Desktop

Import the lab source folder directly:

biosimulant labs import labs/niederer2006-myocyte-relaxation

Then open the imported lab and press Run. The results should include the state-variable timeseries and the What Happened Q&A table.

Notes

• The bundled run uses a baseline plus challenge scenario so the first visualization shows a physiological perturbation without changing the upstream SBML equations.
• Default run length is 1 s with a 0.001 s communication step. These are conservative defaults chosen by category (single-cell electrophysiology vs whole-system circulation vs slow regulatory loop). Tune them in lab.yaml.
• Requires tellurium==2.2.11.2. The first import compiles the SBML to LLVM in-process.
• License: CC0 (from upstream BioModels entry biomodels_ebi:MODEL8687196544).
• This wrapper does not modify the upstream biology. To change rates, initial conditions, or kinetic laws, edit the SBML file in models/core/data/MODEL8687196544.xml directly.

Advanced SBML Identifiers

SBML Parameters

Full list of global parameter IDs, for use with parameter_overrides. Defaults come from the upstream SBML.

Parameter ID	Name	Default Value
Ca_i	Ca_i	(unset)
Ca_b	Ca_b	(unset)
TRPN	TRPN	0.067593139865
z	z	0.014417937837
z_max	z_max	(unset)
alpha_0	alpha_0	0.008
alpha_r1	alpha_r1	0.002
alpha_r2	alpha_r2	0.00175
n_Rel	n_Rel	3
K_z	K_z	0.15
n_Hill	n_Hill	3
Ca_50ref	Ca_50ref	0.00105
z_p	z_p	0.85
beta_1	beta_1	-4
Ca_50	Ca_50	(unset)
Ca_TRPN_50	Ca_TRPN_50	(unset)
K_2	K_2	(unset)
K_1	K_1	(unset)
alpha_Tm	alpha_Tm	(unset)
beta_Tm	beta_Tm	(unset)
J_TRPN	J_TRPN	(unset)
Ca_TRPN_Max	Ca_TRPN_Max	0.07
k_off	k_off	(unset)
k_on	k_on	100
k_Ref_off	k_Ref_off	0.2
gamma_trpn	gamma_trpn	2
lambda	lambda	(unset)
ExtensionRatio	ExtensionRatio	(unset)
dExtensionRatiodt	dExtensionRatiodt	(unset)
lambda_prev	lambda_prev	(unset)
overlap	overlap	(unset)
beta_0	beta_0	4.9
T_ref	T_ref	56.2
T_Base	T_Base	(unset)
T_0	T_0	(unset)
Q	Q	(unset)
a	a	0.35
Q_1	Q_1	0
Q_2	Q_2	0
Q_3	Q_3	0
A_1	A_1	-29
A_2	A_2	138
A_3	A_3	129
alpha_1	alpha_1	0.03
alpha_2	alpha_2	0.13
alpha_3	alpha_3	0.625
Tension	Tension	(unset)

SBML Species

Full list of species IDs, for use with initial_conditions.

No species declared in this SBML file.

{
  "io": {
    "inputs": [
      {
        "name": "integration_step",
        "maps_to": "niederer2006_myocyte_relaxation_core.integration_step"
      },
      {
        "name": "parameter_overrides",
        "maps_to": "niederer2006_myocyte_relaxation_core.parameter_overrides"
      },
      {
        "name": "initial_conditions",
        "maps_to": "niederer2006_myocyte_relaxation_core.initial_conditions"
      }
    ],
    "outputs": [
      {
        "name": "scenario_metadata",
        "maps_to": "niederer2006_myocyte_relaxation_scenario.scenario_metadata"
      },
      {
        "name": "state",
        "maps_to": "niederer2006_myocyte_relaxation_core.state"
      },
      {
        "name": "run_summary",
        "maps_to": "niederer2006_myocyte_relaxation_viz.run_summary"
      }
    ]
  },
  "title": "Niederer2006 Myocyte Relaxation Lab",
  "models": [
    {
      "path": "owned/models/niederer2006_myocyte_relaxation_scenario",
      "alias": "niederer2006_myocyte_relaxation_scenario",
      "parameters": {
        "schedule": {
          "cooperativity": {
            "unit": "dimensionless",
            "label": "cooperativity",
            "active": false,
            "baseline": 0.008,
            "recovery": 0.008,
            "challenge": 0.008,
            "description": "Scenario-controlled cooperativity value."
          },
          "troponin_pool": {
            "unit": "dimensionless",
            "label": "troponin pool",
            "active": true,
            "baseline": 0.067593139865,
            "recovery": 0.067593139865,
            "challenge": 0.1013897097975,
            "description": "Scenario-controlled troponin pool value."
          },
          "relaxation_rate": {
            "unit": "dimensionless",
            "label": "relaxation rate",
            "active": false,
            "baseline": 0.002,
            "recovery": 0.002,
            "challenge": 0.002,
            "description": "Scenario-controlled relaxation rate value."
          }
        },
        "scenario_name": "Troponin pool challenge",
        "baseline_until": 0.25,
        "challenge_until": 0.75,
        "scenario_description": "Baseline is followed by a troponin pool challenge and recovery period."
      },
      "provenance": {
        "owned_path": "owned/models/niederer2006_myocyte_relaxation_scenario"
      }
    },
    {
      "path": "owned/models/niederer2006_myocyte_relaxation_core",
      "alias": "niederer2006_myocyte_relaxation_core",
      "parameters": {
        "model_path": "data/MODEL8687196544.xml",
        "integration_step": 0.001
      },
      "provenance": {
        "owned_path": "owned/models/niederer2006_myocyte_relaxation_core"
      }
    },
    {
      "path": "owned/models/niederer2006_myocyte_relaxation_viz",
      "alias": "niederer2006_myocyte_relaxation_viz",
      "parameters": {
        "lab_title": "Niederer2006 Myocyte Relaxation",
        "lab_question": "How quickly does the myocyte relax after calcium activation?",
        "integration_step": 0.001
      },
      "provenance": {
        "owned_path": "owned/models/niederer2006_myocyte_relaxation_viz"
      }
    }
  ],
  "wiring": [
    {
      "to": [
        "niederer2006_myocyte_relaxation_core.troponin_pool"
      ],
      "from": "niederer2006_myocyte_relaxation_scenario.troponin_pool"
    },
    {
      "to": [
        "niederer2006_myocyte_relaxation_core.relaxation_rate"
      ],
      "from": "niederer2006_myocyte_relaxation_scenario.relaxation_rate"
    },
    {
      "to": [
        "niederer2006_myocyte_relaxation_core.cooperativity"
      ],
      "from": "niederer2006_myocyte_relaxation_scenario.cooperativity"
    },
    {
      "to": [
        "niederer2006_myocyte_relaxation_viz.scenario_metadata"
      ],
      "from": "niederer2006_myocyte_relaxation_scenario.scenario_metadata"
    },
    {
      "to": [
        "niederer2006_myocyte_relaxation_viz.state"
      ],
      "from": "niederer2006_myocyte_relaxation_core.state"
    },
    {
      "to": [
        "niederer2006_myocyte_relaxation_viz.species_labels"
      ],
      "from": "niederer2006_myocyte_relaxation_core.species_labels"
    }
  ],
  "runtime": {
    "duration": 1,
    "initial_inputs": {
      "niederer2006_myocyte_relaxation_core": {
        "integration_step": 0.001,
        "initial_conditions": {
          "payload": {}
        },
        "parameter_overrides": {
          "payload": {}
        }
      }
    },
    "communication_step": 0.001
  },
  "description": "Two-model lab for Niederer2006 Myocyte Relaxation: a core SBML simulator wired into a dedicated visualization sub-model that turns the raw state into friendly timeseries and a plain-language summary. This a model from the article: A quantitative analysis of cardiac myocyte relaxation: a simulation study. It can be used to explore cardiac dynamics and compare response patterns across conditions.",
  "schema_version": "2.0"
}