dorsal/arxiv
View SchemaThe statistical mechanics of complex signaling networks : nerve growth factor signaling
| Authors | Kevin S. Brown, Colin C. Hill, Guillermo A. Calero, Kelvin H. Lee, James P. Sethna, Richard A. Cerione |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0406043 |
| URL | https://arxiv.org/abs/q-bio/0406043 |
| DOI | 10.1088/1478-3967/1/3/006 |
Abstract
It is becoming increasingly appreciated that the signal transduction systems used by eukaryotic cells to achieve a variety of essential responses represent highly complex networks rather than simple linear pathways. While significant effort is being made to experimentally measure the rate constants for individual steps in these signaling networks, many of the parameters required to describe the behavior of these systems remain unknown, or at best, estimates. With these goals and caveats in mind, we use methods of statistical mechanics to extract useful predictions for complex cellular signaling networks. To establish the usefulness of our approach, we have applied our methods towards modeling the nerve growth factor (NGF)-induced differentiation of neuronal cells. Using our approach, we are able to extract predictions that are highly specific and accurate, thereby enabling us to predict the influence of specific signaling modules in determining the integrated cellular response to the two growth factors. We show that extracting biologically relevant predictions from complex signaling models appears to be possible even in the absence of measurements of all the individual rate constants. Our methods also raise some interesting insights into the design and possible evolution of cellular systems, highlighting an inherent property of these systems wherein particular ''soft'' combinations of parameters can be varied over wide ranges without impacting the final output and demonstrating that a few ''stiff'' parameter combinations center around the paramount regulatory steps of the network. We refer to this property -- which is distinct from robustness -- as ''sloppiness.''
{
"annotation_id": "b456c622-f308-4d9a-84e8-a931b7ba1884",
"date_created": "2026-03-02T18:01:31.875000Z",
"date_modified": "2026-03-02T18:01:31.875000Z",
"file_hash": "b5ef4cf8a636263a60cb17c51c815b840c10f734c572c98885f5ebbe13841e16",
"private": false,
"record": {
"abstract": "It is becoming increasingly appreciated that the signal transduction systems\nused by eukaryotic cells to achieve a variety of essential responses represent\nhighly complex networks rather than simple linear pathways. While significant\neffort is being made to experimentally measure the rate constants for\nindividual steps in these signaling networks, many of the parameters required\nto describe the behavior of these systems remain unknown, or at best,\nestimates. With these goals and caveats in mind, we use methods of statistical\nmechanics to extract useful predictions for complex cellular signaling\nnetworks. To establish the usefulness of our approach, we have applied our\nmethods towards modeling the nerve growth factor (NGF)-induced differentiation\nof neuronal cells. Using our approach, we are able to extract predictions that\nare highly specific and accurate, thereby enabling us to predict the influence\nof specific signaling modules in determining the integrated cellular response\nto the two growth factors. We show that extracting biologically relevant\npredictions from complex signaling models appears to be possible even in the\nabsence of measurements of all the individual rate constants. Our methods also\nraise some interesting insights into the design and possible evolution of\ncellular systems, highlighting an inherent property of these systems wherein\nparticular \u0027\u0027soft\u0027\u0027 combinations of parameters can be varied over wide ranges\nwithout impacting the final output and demonstrating that a few \u0027\u0027stiff\u0027\u0027\nparameter combinations center around the paramount regulatory steps of the\nnetwork. We refer to this property -- which is distinct from robustness -- as\n\u0027\u0027sloppiness.\u0027\u0027",
"arxiv_id": "q-bio/0406043",
"authors": [
"Kevin S. Brown",
"Colin C. Hill",
"Guillermo A. Calero",
"Kelvin H. Lee",
"James P. Sethna",
"Richard A. Cerione"
],
"categories": [
"q-bio.MN"
],
"doi": "10.1088/1478-3967/1/3/006",
"title": "The statistical mechanics of complex signaling networks : nerve growth factor signaling",
"url": "https://arxiv.org/abs/q-bio/0406043"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "dac039f4-2e2f-4d35-8cda-5e70d3ba5b8d",
"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
},
"user_id": 1000002
}