Abstract
Cells operate in noisy molecular environments via complex regulatory networks. It is possible to understand how molecular counts are related to noise in specific networks, but it is not generally clear how noise relates to network complexity, because different levels of complexity also imply different overall number of molecules. For a fixed function, does increased network complexity reduce noise, beyond the mere increase of overall molecular counts? If so, complexity could provide an advantage counteracting the costs involved in maintaining larger networks. For that purpose, we investigate how noise affects multistable systems, where a small amount of noise could lead to very different outcomes; thus we turn to biochemical switches. Our method for comparing networks of different structure and complexity is to place them in conditions where they produce exactly the same deterministic function. We are then in a good position to compare their noise characteristics relatively to their identical deterministic traces. We show that more complex networks are better at coping with both intrinsic and extrinsic noise. Intrinsic noise tends to decrease with complexity, and extrinsic noise tends to have less impact. Our findings suggest a new role for increased complexity in biological networks, at parity of function.
Highlights
Cells operate in noisy molecular environments via complex regulatory networks
We showed that by adding a feedback loop to cycle switch network (CC) that is known to exist in biological networks[10], we could improve the correspondence between the biological network and AM, suggesting that the cell cycle switch can achieve the theoretical AM-class performance
We compare noise in the networks by the central limit approximation (CLA), which becomes more accurate for increasing molecule counts
Summary
Cells operate in noisy molecular environments via complex regulatory networks. It is possible to understand how molecular counts are related to noise in specific networks, but it is not generally clear how noise relates to network complexity, because different levels of complexity imply different overall number of molecules. In a recent paper[7] two of the authors describe how a classical cell-cycle switch network (CC)[8] approximates the function of a simpler network independently studied in distributed computing: the Approximate Majority algorithm (AM)[9]. We showed that by adding a feedback loop to CC that is known to exist in biological networks[10], we could improve the correspondence between the biological network and AM, suggesting that the cell cycle switch can achieve the theoretical AM-class performance. Recent experimental work[11] has shown that the additional feedback loop (involving the Greatwall kinase) is necessary for the biological function of the cell cycle switch, reinforcing the relationship between biological and computational networks. Along the way we show exact correspondence with other networks that have more direct biological significance than AM, including various symmetry breaking networks[13]
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