We study a broad class of epitaxial assembly of filament networks on lattice surfaces. Over time, a scale-free behavior emerges with a 2.5-3 power-law exponent in filament length distribution. Partitioning between the power-law and exponential behaviors in a network can be used to find the stage and kinetic parameters of the assembly process. To analyze real-world networks, we develop a computer program that measures the network architecture in experimental images. Application to triaxial networks of collagen fibrils shows quantitative agreement with our model. Our unifying approach can be used for characterizing and controlling the network formation that is observed across biological and nonbiological systems.

An important step towards achieving functional diversity of biomimetic surfaces is to better understand the co-assembly of the extracellular matrix components. For this, we study type-I and type-III collagen, the two major collagen types in the extracellular matrix. By using atomic force microscopy, custom image analysis, and kinetic modeling, we study their homotypic and heterotypic assembly. We find that the growth rate and thickness of heterotypic fibrils decrease as the fraction of type-III collagen increases, but the fibril nucleation rate is maximal at an intermediate fraction of type-III. This is because the more hydrophobic type-I collagen nucleates fast and grows in both longitudinal and lateral directions, whereas more hydrophilic type-III limits lateral growth of fibrils, driving more monomers to nucleate additional fibrils. This demonstrates that subtle differences in physico-chemical properties of similar molecules can be used to fine-tune their assembly behavior.

No abstract

MicroRNAs (miRNA) are a type of non-coding RNA molecules that are effective on the formation and the progression of many different diseases. Various researches have reported that miRNAs play a major role in the prevention, diagnosis, and treatment of complex human diseases. In recent years, researchers have made a tremendous effort to find the potential relationships between miRNAs and diseases. Since the experimental techniques used to find that new miRNA-disease relationships are time-consuming and expensive, many computational techniques have been developed. In this study, Weighted [Formula: see text]-Nearest Known Neighbors and Network Consistency Projection techniques were suggested to predict new miRNA-disease relationships using various types of knowledge such as known miRNA-disease relationships, functional similarity of miRNA, and disease semantic similarity. An average AUC of 0.9037 and 0.9168 were calculated in our method by 5-fold and leave-one-out cross validation, respectively. Case studies of breast, lung, and colon neoplasms were applied to prove the performance of our proposed technique, and the results confirmed the predictive reliability of this method. Therefore, reported experimental results have shown that our proposed method can be used as a reliable computational model to reveal potential relationships between miRNAs and diseases.