Complex, non-monotonic dose-response curves with multiple maxima: Do we (ever) sample densely enough?

A unified approach to dissecting biphasic responses in cell signaling

Biphasic responses are encountered at all levels in biological systems. At the cellular level, biphasic dose-responses are widely encountered in cell signaling and post-translational modification systems and represent safeguards against overactivation or overexpression of species. In this paper, we provide a unified theoretical synthesis of biphasic responses in cell signaling systems, by assessing signaling systems ranging from basic biochemical building blocks to canonical network structures to well-characterized exemplars on one hand, and examining different types of doses on the other. By using analytical and computational approaches applied to a range of systems across levels (described by broadly employed models), we reveal (i) design principles enabling the presence of biphasic responses, including in almost all instances, an explicit characterization of the parameter space (ii) structural factors which preclude the possibility of biphasic responses (iii) different combinations of the presence or absence of enzyme-biphasic and substrate-biphasic responses, representing safeguards against overactivation and overexpression, respectively (iv) the possibility of broadly robust biphasic responses (v) the complete alteration of signaling behavior in a network due to biphasic interactions between species (biphasic regulation) (vi) the propensity of different co-existing biphasic responses in the Erk signaling network. These results both individually and in totality have a number of important consequences for systems and synthetic biology.

Role of Ascaridole and p-Cymene in the Sleep-Promoting Effects of Dysphania ambrosioides Essential Oil via the GABAergic System in a ddY Mouse Inhalation Model

The essential oil obtained from Dysphania ambrosioides leaves (DAEO) has antifungal, antioxidant, and antimicrobial properties. This study investigated DAEO's chemical composition and its sleep-promoting effects via administration by inhalation in ddY mice. Ascaridole (35.5%) and p-cymene (47.2%) were the major components. To obtain insight into DAEO's effects on the central nervous system (CNS), ascaridole and p-cymene were evaluated for sedative activity by using the caffeine-treated excitatory mouse model. DAEO administration significantly decreased locomotor activity at all doses except 0.000 04 mg per 400 μL of triethyl citrate. Both ascaridole and p-cymene were highly effective in decreasing locomotor activity of excited mice by more than 50%. In addition, ascaridole and p-cymene prolonged the pentobarbital-induced sleeping duration by 42% and 77%, respectively. These effects were antagonized by coadministration of gamma-aminobutyric acid (GABA_A)-benzodiazepine receptor antagonist, flumazenil (3 mg/kg), indicating that the GABAergic system mediates the sedative effect. Finally, inhaled ascaridole and p-cymene had no negative effect on motor coordination, as observed during the Rota-rod test. Therefore, via activation of the GABAergic system, ascaridole and p-cymene mediate the sleep-promoting effect of DAEO. The results further extend the knowledge on their use as potential promising natural products for the management of sleep disorders and CNS-related ailments.

Steady-State Differential Dose Response in Biological Systems

In pharmacology and systems biology, it is a fundamental problem to determine how biological systems change their dose-response behavior upon perturbations. In particular, it is unclear how topologies, reactions, and parameters (differentially) affect the dose response. Because parameters are often unknown, systematic approaches should directly relate network structure and function. Here, we outline a procedure to compare general non-monotone dose-response curves and subsequently develop a comprehensive theory for differential dose responses of biochemical networks captured by non-equilibrium steady-state linear framework models. Although these models are amenable to analytical derivations of non-equilibrium steady states in principle, their size frequently increases (super) exponentially with model size. We extract general principles of differential responses based on a model's graph structure and thereby alleviate the combinatorial explosion. This allows us, for example, to determine reactions that affect differential responses, to identify classes of networks with equivalent differential, and to reject hypothetical models reliably without needing to know parameter values. We exemplify such applications for models of insulin signaling.