RESUMEN
Building data-driven models is an effective strategy for information extraction from empirical data. Adapting model parameters specifically to data with a best fitting approach encodes the relevant information into a mathematical model. Subsequently, an optimal control framework extracts the most efficient targets to steer the model into desired changes via external stimuli. The DataXflow software framework integrates three software pipelines, D2D for model fitting, a framework solving optimal control problems including external stimuli and JimenaE providing graphical user interfaces to employ the other frameworks lowering the barriers for the need of programming skills, and simultaneously automating reoccurring modeling tasks. Such tasks include equation generation from a graph and script generation allowing also to approach systems with many agents, like complex gene regulatory networks. A desired state of the model is defined, and therapeutic interventions are modeled as external stimuli. The optimal control framework purposefully exploits the model-encoded information by providing those external stimuli that effect the desired changes most efficiently. The implementation of DataXflow is available under https://github.com/MarvelousHopefull/DataXflow. We showcase its application by detecting specific drug targets for a therapy of lung cancer from measurement data to lower proliferation and increase apoptosis. By an iterative modeling process refining the topology of the model, the regulatory network of the tumor is generated from the data. An application of the optimal control framework in our example reveals the inhibition of AURKA and the activation of CDH1 as the most efficient drug target combination. DataXflow paves the way to an agile interplay between data generation and its analysis potentially accelerating cancer research by an efficient drug target identification, even in complex networks.
RESUMEN
To elucidate the adaptation of the right ventricle to acute and intermittently sustained afterload elevation, targeted preload reductions and afterload increases were implemented in a porcine model involving 12 pigs. Preload reduction was achieved via balloon occlusion of the inferior vena cava before, immediately and 5 min after acute afterload elevation induced by pulmonary artery occlusion or thromboxane A2 analog (U46619) infusion. Ventricular response was monitored by registration of pressure-volume (PV) loops using a conductance catheter. The end-systolic pressure-volume relationship (ESPVR) during pure preload reduction was adequately described by linear regression (mean and SEM slope of ESPVR (Ees) 0.414 ± 0.064 mmHg/ml), reflecting the classical Frank-Starling mechanism (FSM). The ESPVR during acute afterload elevation exhibited a biphasic trajectory with significantly distinct slopes (mean and SEM Ees bilin1: 1.256 ± 0.066 mmHg ml; Ees bilin2: 0.733 ± 0.063 mmHg ml, p < 0.001). The higher slope during the first phase in the absence of ventricular dilation could be explained by a reduced amount of shortening deactivation (SDA). The changes in PV-loops during the second phase were similar to those observed with a preload intervention. The persistent increase in afterload resulted in an increase in the slopes of ESPVR and preload recruitable stroke work (PRSW) with a slight decrease in filling state, indicating a relevant Anrep effect. This effect became more pronounced after 5 min or TXA infusion. This study demonstrates, for the first time, the relevance of intrinsic mechanisms of cardiac autoregulation in the right ventricle during the adaptation to load. The SDA, FSM, and Anrep effect could be differentiated and occurred successively, potentially with some overlap. Notably, the Anrep effect serves to prevent ventricular dilation.