Many intracellular signaling pathways are composed of molecular switches, proteins that transition between two states—on and off. Typically, signaling is initiated when an external stimulus activates its cognate receptor that, in turn, causes downstream switches to transition from off to on using one of the following mechanisms: activation, in which the transition rate from the off state to the on state increases; derepression, in which the transition rate from the on state to the off state decreases; and concerted, in which activation and derepression operate simultaneously. We use mathematical modeling to compare these signaling mechanisms in terms of their dose–response curves, response times, and abilities to process upstream fluctuations. Our analysis elucidates several operating principles for molecular switches. First, activation increases the sensitivity of the pathway, whereas derepression decreases sensitivity. Second, activation generates response times that decrease with signal strength, whereas derepression causes response times to increase with signal strength. These opposing features allow the concerted mechanism to not only show dose–response alignment, but also to decouple the response time from stimulus strength. However, these potentially beneficial properties come at the expense of increased susceptibility to upstream fluctuations. We demonstrate that these operating principles also hold when the models are extended to include additional features, such as receptor removal, kinetic proofreading, and cascades of switches. In total, we show how the architecture of molecular switches govern their response properties. We also discuss the biological implications of our findings. Significance Molecular switches are key components of intracellular signal-transduction pathways. Experimental work has established that the activity of molecular switches is regulated by three distinct mechanisms: 1) activation, 2) derepression, and 3) concerted (activation and derepression). However, it remains unclear how the choice of signaling mechanism influences the performance properties of the switch. Here, we characterize each switch design in terms of dose–response relationship, response time, and abilities to process upstream fluctuations. Our results highlight unique features of each mechanism and provide insight into the operating principles that underlie many different signaling pathways.

We present a model for the effects of ligands on information transmission in G-Protein Coupled Receptor (GPCR) complexes. The model is builtab initioentirely on principles of statistical mechanics and tenets of information transmission theory and was validated in part using agonist-induced effector activity and signaling bias for the angiotensin- and adrenergic-mediated signaling pathways, within vitroobservations of phosphorylation sites on the C tail of the GPCR complex, and single-cell information-transmission experiments. The model extends traditional kinetic models that form the basis for many existing models of GPCR signaling. It is based on maximizing the rates of entropy production and information transmission through the GPCR complex. The model predicts that (1) phosphatase-catalyzed reactions, as opposed to kinase-catalyzed reactions, on the C-tail and internal loops of the GPCR are responsible for controlling the signaling activity, (2) signaling favors the statistical balance of the number of switches in the ON state and the number in the OFF state, and (3) biased-signaling response depends discontinuously on ligand concentration.

G protein-coupled receptors (GPCRs) are the largest class of receptors in the human genome and constitute about 30% of all drug targets. In this article, intended for a non-mathematical audience, both experimental observations and new theoretical results are compared in the context of information transmission across the cell membrane. The amount of information actually currently used or projected to be used in clinical settings is a small fraction of the information transmission capacity of the GPCR. This indicates that the number of yet undiscovered drug targets within GPCRs is much larger than what is currently known. Theoretical studies with some experimental validation indicate that localized heat deposition and dissipation are key to the identification of sites and mechanisms for drug action.

In medicine, dynamic treatment regimes (DTRs) have emerged to guide personalized treatment decisions for patients, accounting for their unique characteristics. However, existing methods for determining optimal DTRs face limitations, often due to reliance on linear models unsuitable for complex disease analysis and a focus on outcome prediction over treatment effect estimation. To overcome these challenges, decision tree-based reinforcement learning approaches have been proposed. Our study aims to evaluate the performance and feasibility of such algorithms: tree-based reinforcement learning (T-RL), DTR-Causal Tree (DTR-CT), DTR-Causal Forest (DTR-CF), stochastic tree-based reinforcement learning (SL-RL), and Q-learning with Random Forest. Using real-world clinical data, we conducted experiments to compare algorithm performances. Evaluation metrics included the proportion of correctly assigned patients to recommended treatments and the empirical mean with standard deviation of expected counterfactual outcomes based on estimated optimal treatment strategies. This research not only highlights the potential of decision tree-based reinforcement learning for dynamic treatment regimes but also contributes to advancing personalized medicine by offering nuanced and effective treatment recommendations.

We present a general theoretical framework for molecular computation in biological systems and apply it to G-protein-coupled receptors (GPCRs), which serve as central regulators of cellular information processing. Despite their importance, the physical principles underlying GPCR switching remain incompletely understood. Using nonequilibrium thermodynamics, we construct a model that identifies the parameters governing receptor-state transitions. The framework shows that the configuration of the switch is governed by two factors: the ATP/GTP-driven chemical flux through the receptor complex and the free-energy difference between competing switch states. The model predicts that GPCRs can occupy three quasistable configurations, corresponding to "on," "off," and an intermediate state, each representing a local maximum in information transmission. Switch states can also be characterized by whether the switch supports a net chemical flux. Active states support sustained chemical flux, whereas inactive states do not. The model incorporates reciprocal conformation-fit changes between ligand and receptor. As such, the model predicts that phosphatase activity, represented as an effective energy barrier, primarily determines whether the switch occupies the "on" or "off" state, whereas kinase activity maintains flux without directly setting state occupancy. These predictions based on experimental evidence point to new targets for drug design. Comparison with label-free impedance measurements supports the existence of multiple quasistable states that depend on ligand conformation. Because the framework relies on general nonequilibrium principles rather than system-specific biochemistry, it extends naturally to other biological switching systems driven by chemical flux.

Introduction: Current guidelines recommend renin angiotensin system inhibitors (RASi) as key components of treatment of diabetic kidney disease (DKD). Additional options include sodium-glucose cotransporter-2 inhibitors (SGLT2i), glucagon-like peptide 1 receptor agonists (GLP1a), and mineralocorticoid receptor antagonists (MCRa). The identification of the optimum drug combination for an individual is difficult because of the inter-, and longitudinal intra-individual heterogeneity of response to therapy.Results: Using data from a large observational study (PROVALID), we identified a set of parameters that can be combined into a meaningful composite biomarker that appears to be able to identify which of the various treatment options is clinically beneficial for an individual. It uses machine-earning techniques to estimate under what conditions a treatment of RASi plus an additional treatment is different from the treatment with RASi alone. The measure of difference is the annual percent change (ΔeGFR) in the estimated glomerular filtration rate (ΔeGFR). The 1eGFR is estimated for both the RASi-alone treatment and the add-on treatment.Discussion: Higher estimated increase of eGFR for add-on patients compared with RASi-alone patients indicates that prognosis may be improved with the add-on treatment. The personalized biomarker value thus identifies which patients may benefit from the additional treatment.