Pith Number

pith:R6QZASJZ

pith:2026:R6QZASJZS2ZHNWOSET5B7SI2UL

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Variance-Aware Estimation and Inference for Michaelis--Menten Models with Heteroscedastic Errors and Clustered Measurements

Ah Young Jeong, Mijeong Kim, Minkyoung Cha

A variance-aware procedure using simple working models stabilizes Michaelis-Menten estimates of Km and Vmax when errors vary with concentration or data are clustered.

arxiv:2605.13168 v1 · 2026-05-13 · stat.ME

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Claims

C1strongest claim

simple variance-function and covariance modeling can stabilize original-scale Michaelis--Menten inference when variability changes with substrate concentration or measurements are clustered.

C2weakest assumption

The prespecified working variance functions and conditionally Gaussian models sufficiently approximate the true error distribution and clustering structure without biasing the parameter estimates for Km and Vmax.

C3one line summary

A new estimation method for Michaelis-Menten models handles varying variance and clustered data through root-finding and plug-in updates, improving inference in simulations and real enzyme data.

References

25 extracted · 25 resolved · 0 Pith anchors

[1] L. Michaelis, M. L. Menten, Die kinetik der invertinwirkung, Biochem. Z. 49 (1913) 333–369 1913

[2] K.A.Johnson, R.S.Goody, TheoriginalMichaelisconstant: translation of the 1913 Michaelis–Menten paper, Biochemistry 50 (39) (2011) 8264– 8269.doi:10.1021/bi201284u 1913 · doi:10.1021/bi201284u

[3] G. N. Wilkinson, Statistical estimations in enzyme kinetics, Biochem. J. 80 (2) (1961) 324–332.doi:10.1042/BJ0800324 1961 · doi:10.1042/bj0800324

[4] R. Eisenthal, A. Cornish-Bowden, The direct linear plot: A new graph- ical procedure for estimating enzyme kinetic parameters, Biochem. J. 139 (3) (1974) 715–720.doi:10.1042/BJ1390715 1974 · doi:10.1042/bj1390715

[5] A. Cornish-Bowden, R. Eisenthal, Statistical considerations in the esti- mation of enzyme kinetic parameters by the direct linear plot and other methods, Biochem.J.139(3)(1974)721–730.doi:10.1042/BJ13 1974 · doi:10.1042/bj1390721

Receipt and verification

First computed	2026-05-18T03:08:56.662895Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

8fa190493996b276d9d224fa1fc91aa2c3edfd1a7a1312cbc37ffc7ecb95df24

Aliases

arxiv: 2605.13168 · arxiv_version: 2605.13168v1 · doi: 10.48550/arxiv.2605.13168 · pith_short_12: R6QZASJZS2ZH · pith_short_16: R6QZASJZS2ZHNWOS · pith_short_8: R6QZASJZ

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/R6QZASJZS2ZHNWOSET5B7SI2UL \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 8fa190493996b276d9d224fa1fc91aa2c3edfd1a7a1312cbc37ffc7ecb95df24

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "c9577284f891672de977acfdd19debabb8903b39172c246c218089e452c62fdb",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "stat.ME",
    "submitted_at": "2026-05-13T08:30:44Z",
    "title_canon_sha256": "8ca80557e9389a3ef95907e09a623541ec010085a551ee9551d5a7aaac532fc1"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13168",
    "kind": "arxiv",
    "version": 1
  }
}