One of the central functions of actin cytoskeleton is to provide the mechanical support required for the establishment and maintenance of cell morphology. The mechanical properties of actin filament assemblies are a consequence of both the available polymer concentration and the actin regulatory proteins that direct the formation of higher order structures. By monitoring the displacement of well-dispersed microspheres via fluorescence microscopy, we probe the degree of spatial heterogeneity of F-actin gels and networks in vitro. We compare the distribution of the time-dependent mean-square displacement (MSD) of polystyrene microspheres imbedded in low- and high-concentration F-actin solutions, in the presence and absence of the F-actin-bundling protein fascin. The MSD distribution of a 2. 6-microM F-actin solution is symmetric and its standard deviation is similar to that of a homogeneous solution of glycerol of similar zero-shear viscosity. However, increasing actin concentration renders the MSD distribution wide and asymmetric, an effect enhanced by fascin. Quantitative changes in the shape of the MSD distribution correlate qualitatively with the presence of large heterogeneities in F-actin solutions produced by increased filament concentration and the presence of actin bundles, as detected by confocal microscopy. Multiple-particle tracking offers a new, quantitative method to characterize the organization of biopolymers in solution.

Summary Computational models are increasingly used to understand and predict complex biological phenomena. These models contain many unknown parameters, at least some of which are difficult to measure directly, and instead are estimated by fitting to time-course data. Previous work has suggested that even with precise data sets, many parameters are unknowable by trajectory measurements. We examined this question in the context of a pathway model of epidermal growth factor (EGF) and neuronal growth factor (NGF) signaling. Computationally, we examined a palette of experimental perturbations that included different doses of EGF and NGF as well as single and multiple gene knockdowns and overexpressions. While no single experiment could accurately estimate all of the parameters, experimental design methodology identified a set of five complementary experiments that could. These results suggest optimism for the prospects for calibrating even large models, that the success of parameter estimation is intimately linked to the experimental perturbations used, and that experimental design methodology is important for parameter fitting of biological models and likely for the accuracy that can be expected for them.

Mechanism-based chemical kinetic models are increasingly being used to describe biological signaling. Such models serve to encapsulate current understanding of pathways and to enable insight into complex biological processes. One challenge in model development is that, with limited experimental data, multiple models can be consistent with known mechanisms and existing data. Here, we address the problem of model ambiguity by providing a method for designing dynamic stimuli that, in stimulus–response experiments, distinguish among parameterized models with different topologies, i.e., reaction mechanisms, in which only some of the species can be measured. We develop the approach by presenting two formulations of a model-based controller that is used to design the dynamic stimulus. In both formulations, an input signal is designed for each candidate model and parameterization so as to drive the model outputs through a target trajectory. The quality of a model is then assessed by the ability of the corresponding controller, informed by that model, to drive the experimental system. We evaluated our method on models of antibody–ligand binding, mitogen-activated protein kinase (MAPK) phosphorylation and de-phosphorylation, and larger models of the epidermal growth factor receptor (EGFR) pathway. For each of these systems, the controller informed by the correct model is the most successful at designing a stimulus to produce the desired behavior. Using these stimuli we were able to distinguish between models with subtle mechanistic differences or where input and outputs were multiple reactions removed from the model differences. An advantage of this method of model discrimination is that it does not require novel reagents, or altered measurement techniques; the only change to the experiment is the time course of stimulation. Taken together, these results provide a strong basis for using designed input stimuli as a tool for the development of cell signaling models.

The coronavirus disease 2019 (COVID‐19) pandemic has caused respiratory failure and associated mortality in numbers that have overwhelmed global health systems. Thrombotic coagulopathy is present in nearly three quarters of patients with COVID‐19 admitted to the intensive care unit, and both the clinical picture and pathologic findings are consistent with microvascular occlusive phenomena being a major contributor to their unique form of respiratory failure. Numerous studies are ongoing focusing on anticytokine therapies, antibiotics, and antiviral agents, but none to date have focused on treating the underlying thrombotic coagulopathy in an effort to improve respiratory failure in COVID‐19. There are animal data and a previous human trial demonstrating a survival advantage with fibrinolytic therapy to treat acute respiratory distress syndrome. Here, we review the extant and emerging literature on the relationship between thrombotic coagulopathy and pulmonary failure in the context of COVID‐19 and present the scientific rationale for consideration of targeting the coagulation and fibrinolytic systems to improve pulmonary function in these patients.

CX‑072 is an anti‑PD‑L1 (programmed death ligand 1) Probody therapeutic (Pb‐Tx) designed to be preferentially activated by proteases in the tumor microenvironment and not in healthy tissue. Here, we report the model‐informed drug development of CX‐072. A quantitative systems pharmacology (QSP) model that captured known mechanisms of Pb‐Tx activation, biodistribution, elimination, and target engagement was used to inform clinical translation. The QSP model predicted that a trough level of masked CX‐072 (intact CX‐072) of 13–99 nM would correspond to a targeted, 95% receptor occupancy in the tumor. The QSP model predictions appeared consistent with preliminary human single‑dose pharmacokinetic (PK) data following CX‐072 0.03–30.0 mg/kg as monotherapy: CX‑072 circulated predominantly as intact CX‐072 with minimal evidence of target‐mediated drug disposition. A preliminary population PK (POPPK) analysis based upon 130 subjects receiving 0.03–30.0 mg/kg as monotherapy included a provision for a putative time‐dependent and dose‐dependent antidrug antibody (ADA) effect on clearance (CL) with a mixture model. Preliminary POPPK estimates for intact CX‐072 time‐invariant CL and volume of distribution were 0.306 L/day and 4.84 L, respectively. Exposure–response analyses did not identify statistically significant relationships with best change from baseline sum of measurements and either adverse events of grade ≥ 3 or of special interest. Simulations suggested that > 95% of patients receiving CX‐072 10 mg/kg every two weeks would exceed the targeted trough level regardless of ADA, and that dose adjustment by body weight was not necessary, supporting a fixed 800 mg dose for evaluation in phase II.