Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Emergence of cooperative bistability and robustness of gene regulatory
  networks   Gene regulatory networks (GRNs) are complex systems in which many genes
regulate mutually to adapt the cell state to environmental conditions. In
addition to function, the GRNs possess several kinds of robustness. This
robustness means that systems do not lose their functionality when exposed to
disturbances such as mutations or noise, and is widely observed at many levels
in living systems. Both function and robustness have been acquired through
evolution. In this respect, GRNs utilized in living systems are rare among all
possible GRNs. In this study, we explored the fitness landscape of GRNs and
investigated how robustness emerged in highly-fit GRNs. We considered a toy
model of GRNs with one input gene and one output gene. The difference in the
expression level of the output gene between two input states, "on" and "off",
was considered as fitness. Thus, the determination of the fitness of a GRN was
based on how sensitively it responded to the input.We employed the
multicanonical Monte Carlo method, which can sample GRNs randomly in a wide
range of fitness levels, and classified the GRNs according to their fitness.
The following properties were found: (1) Highly-fit GRNs exhibited bistability
for intermediate input between "on" and "off". This means that such GRNs
responded to two input states by using different fixed points of dynamics. This
bistability emerges necessarily as fitness increases. (2) These highly-fit GRNs
were robust against noise because of their bistability. In other words, noise
robustness is a byproduct of high fitness. (3) GRNs that were robust against
mutations were not extremely rare among the highly-fit GRNs. This implies that
mutational robustness is readily acquired through the evolutionary process.
These properties are universal irrespective of the evolutionary pathway,
because the results do not rely on evolutionary simulation.

2
The categories of this paper are: math
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an