Positive feedback loops have been identified in many biological signal transduction systems. Their importance in a system's bistability has been well established by identifying multiple steady states of a network under different parameters. In this paper, we identify the contribution of positive feedback loops to network robustness by a systematic comparison between network structures and responses to perturbations at a pre-steady state. Our study is based on a T helper (Th) cell differentiation model in which positive feedback loops give rise to a subnet robustness against both positive and negative perturbations from outside the subnet. Although it is unclear whether this pre-steady state exists in vivo, the results from in silico modeling are in agreement with the reported in vivo observations. Being highly heterogeneous and rarely at a steady state, the disease cells, such as cancer cells, may gain potential resistances to certain drugs in a similar way. From the reverse engineering point of view, our results confirm that, while data from perturbation experiments are very effective in identifying causal relationships among the network components, caution should be taken, as in some circumstances, a direct interaction could be invisible due to positive feedback loops.
Asymptotic limit in a cell differentiation model with consideration of transcription