Biology subjects

Politano, G.

Publications and source records attributed to Politano, G..

3 recordsLinked to original sources

bioRxiv2021

Spectracular: minimizing spectral overlap in multicolor flow cytometry experiments

Selecting fluorochromes for polychromatic panels for flow cytometry is complex and time-consuming. Poorly designed panels can result in overlap between the emission spectra of the different fluorochromes, making their signals difficult to separate. While assessing all possible panels is simple to do programmatically, the combinatorial complexity of most real world problems renders brute force computation impractical. Here we present a novel complexity optimization algorithm for fast design of minimal spectral overlap fluorochrome combinations. To aid researchers in designing fluorochrome panels we implemented the algorithm in a web server, Spectracular, that also considers instrument laser configuration and allows users to define various constraints related to antibody-fluorochrome availability and marker co-expression.

bioinformatics↗

bioRxiv2021

Nets-within-nets for modeling emergent patterns in ontogeneticprocesses

Ontogenesis is the development of an organism from its earliest stage to maturity, including homeostasis maintenance throughout adulthood despite environmental perturbations. Almost all cells of a multicellular organism share the same genomic information. Nevertheless, phenotypic diversity and complex supra-cellular architectures emerge at every level, starting from tissues and organs. This is possible thanks to a robust and dynamic interplay of regulative mechanisms. To study ontogenesis, it is necessary to consider different levels of regulation, both genetic and epigenetic. Each cell undergoes a specific path across a landscape of possible regulative states affecting both its structure and its functions during development. This paper proposes using the Nets-Within-Nets formalism, which combines Petri Nets simplicity with the capability to represent and simulate the interplay between different layers of regulation connected by non-trivial and context-dependent hierarchical relations. In particular, this work introduces a modeling strategy based on Nets-Within-Nets that can model several critical processes involved in ontogenesis. Moreover, it presents a case study focusing on the first phase of Vulval Precursor Cells specification in C. Elegans. The case study shows that the proposed model can simulate the emergent morphogenetic pattern corresponding to the observed developmental outcome of that phase, in both the physiological case and different mutations. The model presented in the results section is available online at https://github.com/sysbio-polito/NWN_CElegans_VPC_model/

systems biology↗

bioRxiv2017

Beyond Homology Transfer: Deep Learning for Automated Annotation of Proteins

Accurate annotation of protein functions is important for a profound understanding of molecular biology. A large number of proteins remain uncharacterized because of the sparsity of available supporting information. For a large set of uncharacterized proteins, the only type of information available is their amino acid sequence. In this paper, we propose DeepSeq - a deep learning architecture - that utilizes only the protein sequence information to predict its associated functions. The prediction process does not require handcrafted features; rather, the architecture automatically extracts representations from the input sequence data. Results of our experiments with DeepSeq indicate significant improvements in terms of prediction accuracy when compared with other sequence-based methods. Our deep learning model achieves an overall validation accuracy of 86.72%, with an F1 score of 71.13%. Moreover, using the automatically learned features and without any changes to DeepSeq, we successfully solved a different problem i.e. protein function localization, with no human intervention. Finally, we discuss how this same architecture can be used to solve even more complicated problems such as prediction of 2D and 3D structure as well as protein-protein interactions.

bioinformatics↗