The Role of Mechanical Properties and Structure of Type I Collagen Hydrogels on Colorectal Cancer Cell Migration

Mechanical interactions between cells and their microenvironment play an important role in determining cell fate, which is particularly relevant in metastasis, a process where cells invade tissue matrices with different mechanical properties. In vitro, type I collagen hydrogels have been commonly used for modeling the microenvironment due to its ubiquity in the human body. In this work, the combined influence of the stiffness of these hydrogels and their ultrastructure on the migration patterns of HCT-116 and HT-29 spheroids are analyzed. For this, six different types of pure type I collagen hydrogels by changing the collagen concentration and the gelation temperature are prepared. The stiffness of each sample is measured and its ultrastructure is characterized. Cell migration studies are then performed by seeding the spheroids in three different spatial conditions. It is shown that changes in the aforementioned parameters lead to differences in the mechanical stiffness of the matrices as well as the ultrastructure. These differences, in turn, lead to distinct cell migration patterns of HCT-116 and HT-29 spheroids in either of the spatial conditions tested. Based on these results, it is concluded that the stiffness and the ultrastructural organization of the matrix can actively modulate cell migration behavior in colorectal cancer spheroids.     

The Mechanical and Biological Performance of Photopolymerized Gelatin-Based Hydrogels as a Function of the Reaction Media

From the first experiments with biomaterials to mimic tissue properties, the mechanical and biochemical characterization has evolved extensively. Several properties can be described, however, what should be essential is to conduct a proper and physiologically relevant characterization. Herein, the influence of the reaction media (RM) and swelling media (SM)–phosphate buffered saline (PBS) and Dulbecco's modified Eagle's medium (DMEM) with two different glucose concentrations–is described in gelatin methacrylamide (GelMA) hydrogel mechanics and in the biological behavior of two tumoral cell lines (Caco-2 and HCT-116). All scaffolds are UV-photocrosslinked under identical conditions and evaluated for mass swelling ratio and stiffness. The results indicate that stiffness is highly susceptible to the RM, but not to the SM. Additionally, PBS-prepared hydrogels exhibited a higher photopolymerization degree according to high resolution magic-angle spinning (HR-MAS) NMR. These findings correlate with the biological response of Caco-2 and HCT-116 cells seeded on the substrates, which demonstrated flatter morphologies on stiffer hydrogels. Overall, cell viability and proliferation are excellent for both cell lines, and Caco-2 cells displayed a characteristic apical-basal polarization based on F-actin/Nuclei fluorescence images. These characterization experiments highlight the importance of conducting mechanical testing of biomaterials in the same medium as cell culture.     

PepFun: Open Source Protocols for Peptide-Related Computational Analysis

Peptide research has increased during the last years due to their applications as biomarkers, therapeutic alternatives or as antigenic sub-units in vaccines. The implementation of computational resources have facilitated the identification of novel sequences, the prediction of properties, and the modelling of structures. However, there is still a lack of open source protocols that enable their straightforward analysis. Here, we present PepFun, a compilation of bioinformatics and cheminformatics functionalities that are easy to implement and customize for studying peptides at different levels: sequence, structure and their interactions with proteins. PepFun enables calculating multiple characteristics for massive sets of peptide sequences, and obtaining different structural observables derived from protein-peptide complexes. In addition, random or guided library design of peptide sequences can be customized for screening campaigns. The package has been created under the python language based on built-in functions and methods available in the open source projects BioPython and RDKit. We present two tutorials where we tested peptide binders of the MHC class II and the Granzyme B protease.     

Phase calculation with a swept source in optical coherence tomography: Jitter influence

An analysis of phase modifications introduced by laser jitter in frequency-domain optical coherence tomography (OCT) is described. It is shown that a 1D spurious phase function is introduced as a consequence of this phenomenon. This phase function is added to any other introduced by the object under analysis causing a superposition of both. The result may impose some limitations for unambiguous determination of phase changes of objects. Experimental and theoretical results are presented.     

Schemata, Distributions and Graphical Models in Evolutionary Optimization

In this paper the optimization of additively decomposed discrete functions is investigated. For these functions genetic algorithms have exhibited a poor performance. First the schema theory of genetic algorithms is reformulated in probability theory terms. A schema defines the structure of a marginal distribution. Then the conceptual algorithm BEDA is introduced. BEDA uses a Boltzmann distribution to generate search points. From BEDA a new algorithm, FDA, is derived. FDA uses a factorization of the distribution. The factorization captures the structure of the given function. The factorization problem is closely connected to the theory of conditional independence graphs. For the test functions considered, the performance of FDA—in number of generations till convergence—is similar to that of a genetic algorithm for the OneMax function. This result is theoretically explained.     

Effective learning hyper-heuristics for the course timetabling problem

Course timetabling is an important and recurring administrative activity in most educational institutions. This article combines a general modeling methodology with effective learning hyper-heuristics to solve this problem. The proposed hyper-heuristics are based on an iterated local search procedure that autonomously combines a set of move operators. Two types of learning for operator selection are contrasted: a static (offline) approach, with a clear distinction between training and execution phases; and a dynamic approach that learns on the fly. The resulting algorithms are tested over the set of real-world instances collected by the first and second International Timetabling competitions. The dynamic scheme statistically outperforms the static counterpart, and produces competitive results when compared to the state-of-the-art, even producing a new best-known solution. Importantly, our study illustrates that algorithms with increased autonomy and generality can outperform human designed problem-specific algorithms.