Maximum likelihood estimation of the kinetics of receptor-mediated adhesion

Dynamic biochemical tissue analysis detects functional selectin ligands on human cancer tissues

Cell adhesion mediated by selectins (expressed by activated endothelium, activated platelets, and leukocytes) binding to their resepective selectin ligands (expressed by cancer cells) may be involved in metastasis. Therefore, methods of characterizing selectin ligands expressed on human tissue may serve as valuable assays. Presented herein is an innovative method for detecting functional selectin ligands expressed on human tissue that uses a dynamic approach, which allows for control over the force applied to the bonds between the probe and target molecules. This new method of tissue interrogation, known as dynamic biochemical tissue analysis (DBTA), involves the perfusion of molecular probe-coated microspheres over tissues. DBTA using selectin-coated probes is able to detect functional selectin ligands expressed on tissue from multiple cancer types at both primary and metastatic sites.

Generalized method of moments for estimating parameters of stochastic reaction networks

BACKGROUND

Discrete-state stochastic models have become a well-established approach to describe biochemical reaction networks that are influenced by the inherent randomness of cellular events. In the last years several methods for accurately approximating the statistical moments of such models have become very popular since they allow an efficient analysis of complex networks.

RESULTS

We propose a generalized method of moments approach for inferring the parameters of reaction networks based on a sophisticated matching of the statistical moments of the corresponding stochastic model and the sample moments of population snapshot data. The proposed parameter estimation method exploits recently developed moment-based approximations and provides estimators with desirable statistical properties when a large number of samples is available. We demonstrate the usefulness and efficiency of the inference method on two case studies.

CONCLUSIONS

The generalized method of moments provides accurate and fast estimations of unknown parameters of reaction networks. The accuracy increases when also moments of order higher than two are considered. In addition, the variance of the estimator decreases, when more samples are given or when higher order moments are included.

Exploiting the kinetic interplay between GPIbα-VWF binding interfaces to regulate hemostasis and thrombosis

Platelet-von Willebrand factor (VWF) interactions must be tightly regulated in order to promote effective hemostasis and prevent occlusive thrombus formation. However, it is unclear what role the inherent properties of the bond formed between the platelet receptor glycoprotein Ibα and the A1 domain of VWF play in these processes. Using VWF-A1 knock-in mice with mutations that enhance (I1309V) or disrupt (R1326H) platelet receptor glycoprotein Ibα binding, we now demonstrate that the kinetic interplay between two distinct contact surfaces influences the site and extent to which platelets bind VWF. Incorporation of R1326H mutation into the major site shortened bond lifetime, yielding defects in hemostasis and thrombosis comparable to VWF-deficient animals. Similarly, disrupting this region of contact with an allosteric inhibitor impaired human platelet accrual in damaged arterioles. In contrast, the I1309V mutation near the minor site prolonged bond lifetime, which was essential for the development of a type 2B-like VWD phenotype. However, combining the R1326H and I1309V mutations normalized both bond kinetics and the hemostatic and thrombotic properties of VWF. These findings broaden our understanding of mechanisms governing platelet-VWF interactions in health and disease, and underscore the importance of combined biophysical and genetic approaches in identifying potential therapeutic avenues for treating bleeding and thrombotic disorders.

Approximate maximum likelihood estimation for stochastic chemical kinetics

Recent experimental imaging techniques are able to tag and count molecular populations in a living cell. From these data mathematical models are inferred and calibrated. If small populations are present, discrete-state stochastic models are widely-used to describe the discreteness and randomness of molecular interactions. Based on time-series data of the molecular populations, the corresponding stochastic reaction rate constants can be estimated. This procedure is computationally very challenging, since the underlying stochastic process has to be solved for different parameters in order to obtain optimal estimates. Here, we focus on the maximum likelihood method and estimate rate constants, initial populations and parameters representing measurement errors.