Back

Why Life is Hot

Schilling, T.; Warren, P.; Poon, W. C. K.

2026-02-15 biophysics
10.64898/2026.02.13.705721 bioRxiv
Show abstract

The process of evolution by natural selection leads to phenotypes of increasing fitness. For cellular chemical reaction networks, this means optimising a variety of fitness functions such as robustness, precision, or sensitivity to external stimuli. We argue that these diverse goals can be achieved by a versatile, generic mechanism: coupling chemical reaction networks to reservoirs that are strongly out of equilibrium. Using theory and numerics we show that this mechanism of optimization comes at the price of significant heat dissipation. We compute the heat flux caused by kinetic proof-reading in Escherichia coli and show that it constitutes a significant fraction of the total heat flux experimentally measured in this model organism. We then demonstrate that the degree of optimality achievable saturates, and that Nature appears to operate near saturation despite high energetic costs. We conclude that life is hot largely because of the need for a versatile mechanism to optimise a variety of fitness functions.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
PLOS Computational Biology
● 1863 papers in training set
Top 2%
18.2%
Similar papers in this journal
2
Physical Review E
● 112 papers in training set
Top 0.1%
12.4%
Similar papers in this journal
3
Physical Review Letters
● 47 papers in training set
Top 0.1%
11.7%
Similar papers in this journal
4
Journal of The Royal Society Interface
● 235 papers in training set
Top 0.5%
6.6%
Similar papers in this journal
5
Biophysical Journal
○ 631 papers in training set
Top 1%
6.1%
Similar papers in this journal
50% of probability mass above
6
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 9%
5.4%
7
Physical Review Research
● 49 papers in training set
Top 0.1%
5.4%
8
Physical Review X
● 25 papers in training set
Top 0.1%
4.0%
9
eLife
● 5828 papers in training set
Top 39%
2.7%
10
Nature Communications
○ 5641 papers in training set
Top 40%
2.3%
11
Scientific Reports
○ 3612 papers in training set
Top 43%
2.3%
12
PLOS ONE
● 5266 papers in training set
Top 41%
2.3%
13
Mathematical Biosciences
○ 49 papers in training set
Top 0.5%
2.1%
14
PRX Life
● 42 papers in training set
Top 0.3%
2.1%
15
Royal Society Open Science
● 214 papers in training set
Top 3%
1.9%
16
Physical Biology
○ 46 papers in training set
Top 0.3%
1.9%
17
PNAS Nexus
◐ 159 papers in training set
Top 3%
1.0%
18
Entropy
○ 21 papers in training set
Top 0.3%
0.8%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.