Dynamics robustness of cascading systems

Abstract

A most important property of biochemical systems is robustness. Static robustness, e.g., homeostasis, is the insensitivity of a state against perturbations, whereas dynamics robustness, e.g., homeorhesis, is the insensitivity of a dynamic process. In contrast to the extensively studied static robustness, dynamics robustness, i.e., how a system creates an invariant temporal profile against perturbations, is little explored despite transient dynamics being crucial for cellular fates and are reported to be robust experimentally. For example, the duration of a stimulus elicits different phenotypic responses, and signaling networks process and encode temporal information. Hence, robustness in time courses will be necessary for functional biochemical networks. Based on dynamical systems theory, we uncovered a general mechanism to achieve dynamics robustness. Using a three-stage linear signaling cascade as an example, we found that the temporal profiles and response duration post-stimulus is robust to perturbations against certain parameters. Then analyzing the linearized model, we elucidated the criteria of when signaling cascades will display dynamics robustness. We found that changes in the upstream modules are masked in the cascade, and that the response duration is mainly controlled by the rate-limiting module and organization of the cascade’s kinetics. Specifically, we found two necessary conditions for dynamics robustness in signaling cascades: 1) Constraint on the rate-limiting process: The phosphatase activity in the perturbed module is not the slowest. 2) Constraints on the initial conditions: The kinase activity needs to be fast enough such that each module is saturated even with fast phosphatase activity and upstream changes are attenuated. We discussed the relevance of such robustness to several biological examples and the validity of the above conditions therein. Given the applicability of dynamics robustness to a variety of systems, it will provide a general basis for how biological systems function dynamically.

Scale invariance in natural and artificial collective systems: a review

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Environmental Services: A New Approach Toward Addressing Sustainable Development Goals in Sub-Saharan Africa

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Self-replenishment cycles generate a threshold response

“…Some coenzyme recycling cycles show an amplifier function in a similar manner to that of substrate cycles, because their reaction structures are the same as those of the substrate cycles. Coenzyme recycling cascades have been extensively studied that directly bind multiple metabolic reactions coupled with coenzymes 10,11,21 . These can be classified into two types: the coenzyme recycling cascade with conservation of coenzymes 8,10,22 and those with accumulation of coenzymes, where the coenzymes are supplied from neighboring pathways 11 .…”