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Analyze What Papers Actually Reported

Work with exact reported results from versioned evidence records β€” no guessing from summaries.Know what the science actually supports before you trust the answer.

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     Quick Analysis Plan



    Plan: reimplement Ruoff's coherent-feedback homeostat models (motif-2, motif-8, non-oscillatory variant, incoherent-feedback control) as mass-action ODEs, run step perturbations across constant backgrounds with LSODA integration, and test background compensation by checking whether maximum frequency or Ξ”A is background-independent; separately reproduce the Hill/Naka–Rushton analysis showing retinal parallel curves need no compensation mechanism.


     Long Analysis Plan



    Reproducible Analysis Plan for Ruoff et al., Background Compensation

    Goal: Reproduce and extend the central result that coherent feedback (controllers I1/I2 acting through controlled variable A) yields robust background compensation in oscillatory and non-oscillatory homeostats, while incoherent feedback does not.

    Step 1 β€” Implement models as mass-action ODEs. Code four systems in Python (scipy.integrate.solve_ivp, method='LSODA', matching the paper's LSODE): (a) motif-2 (m2) two-layer negative-feedback oscillator with zero-order removals (Eqs 3–7); (b) motif-8 outflow-control oscillator (Eqs 11–15); (c) non-oscillatory m2 variant with first-order A removal (Eq 18); (d) incoherent-feedback m2 variant (Eqs 27–31).

    Step 2 β€” Perturbation sweeps. For m2: apply k2 steps 1.0β†’2.0 … 1.0β†’10.0 at t=100 across multiple constant k10 backgrounds; record maximum frequency per run. For m8: k1 step 1β†’100 at backgrounds k3 = 0 and 1024. For non-oscillatory: measure Ξ”A (maximum excursion) per k2 step per background. For incoherent: k2 step 1β†’10 at t=500 across backgrounds β€” expect frequency homeostasis but declining max frequency with background (E4). Parallel Ξ”A/frequency-vs-step lines across backgrounds = background compensation.

    Step 3 β€” Retinal parallel-line check. Evaluate V = I/(I+Οƒ) with Vmax=1 and Οƒ spanning 1Γ—10⁻⁴ to 10 on linear, semi-log, and log-log axes; confirm parallel curves arise without any compensation mechanism, contradicting the earlier interpretation of parallel light-adaptation plots as evidence for background compensation.

    Step 4 β€” Validation & falsification. The authors note no known biological example of the property; extend background ranges well beyond those tested β€” if max frequency shifts at higher backgrounds, compensation is not robust. Also test alternative integral-control implementations (e.g., antithetic feedback), which the paper did not cover.

    Known blindspots: results are purely in silico, limited to two motifs with zero-order integral control, and retinal analysis restricted to Hill Ξ±=1 (rods/cones only).



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    Updated: September 21, 2026

     Top Data Sources ExportMCP



     Analysis Wizard



    Building a Python ODE simulation suite reproducing Ruoff's motif-2/motif-8 homeostats, running background-swept perturbation experiments to quantify background compensation.



     Hypothesis Graveyard



    Parallel light-adaptation curves indicate retinal background compensation β€” falsified by the Hill-equation null model showing parallel lines arise without any compensatory mechanism.

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