MAGNETO: Cell type marker panel generator from single-cell transcriptomic data.

Single-cell RNA sequencing experiments produce data useful to identify different cell types, including uncharacterized and rare ones. This enables us to study the specific functional roles of these cells in different microenvironments and contexts. After identifying a (novel) cell type of interest, it is essential to build succinct marker panels, composed of a few genes referring to cell surface proteins and clusters of differentiation molecules, able to discriminate the desired cells from the other cell populations.

Barcode demultiplexing of nanopore sequencing raw signals by unsupervised machine learning.

Oxford Nanopore Technologies (ONT) is a third generation sequencing approach that allows the analysis of individual, full-length nucleic acids. ONT records the alterations of an ionic current flowing across a nano-scaled pore while a DNA or RNA strand is threading through the pore. Basecalling methods are then leveraged to translate the recorded signal back to the nucleic acid sequence.

Modeling Calcium Signaling in Highlights the Role and Regulation of the Calmodulin-Calcineurin Pathway in Response to Hypotonic Shock.

Calcium homeostasis and signaling processes in , as well as in any eukaryotic organism, depend on various transporters and channels located on both the plasma and intracellular membranes. The activity of these proteins is regulated by a number of feedback mechanisms that act through the calmodulin-calcineurin pathway. When exposed to hypotonic shock (HTS), yeast cells respond with an increased cytosolic calcium transient, which seems to be conditioned by the opening of stretch-activated channels.

FiCoS: A fine-grained and coarse-grained GPU-powered deterministic simulator for biochemical networks.

Mathematical models of biochemical networks can largely facilitate the comprehension of the mechanisms at the basis of cellular processes, as well as the formulation of hypotheses that can be tested by means of targeted laboratory experiments. However, two issues might hamper the achievement of fruitful outcomes. On the one hand, detailed mechanistic models can involve hundreds or thousands of molecular species and their intermediate complexes, as well as hundreds or thousands of chemical reactions, a situation generally occurring in rule-based modeling.

ACDC: Automated Cell Detection and Counting for Time-Lapse Fluorescence Microscopy.

Advances in microscopy imaging technologies have enabled the visualization of live-cell dynamic processes using time-lapse microscopy imaging. However, modern methods exhibit several limitations related to the training phases and to time constraints, hindering their application in the laboratory practice. In this work, we present a novel method, named Automated Cell Detection and Counting (ACDC), designed for activity detection of fluorescent labeled cell nuclei in time-lapse microscopy.

Screening for Combination Cancer Therapies With Dynamic Fuzzy Modeling and Multi-Objective Optimization.

Combination therapies proved to be a valuable strategy in the fight against cancer, thanks to their increased efficacy in inducing tumor cell death and in reducing tumor growth, metastatic potential, and the risk of developing drug resistance. The identification of effective combinations of drug targets generally relies on costly and time consuming processes based on experiments. Here, we present a novel computational approach that, by integrating dynamic fuzzy modeling with multi-objective optimization, allows to efficiently identify novel combination cancer therapies, with a relevant saving in working time and costs.

HyperBeta: characterizing the structural dynamics of proteins and self-assembling peptides.

Self-assembling processes are ubiquitous phenomena that drive the organization and the hierarchical formation of complex molecular systems. The investigation of assembling dynamics, emerging from the interactions among biomolecules like amino-acids and polypeptides, is fundamental to determine how a mixture of simple objects can yield a complex structure at the nano-scale level. In this paper we present HyperBeta, a novel open-source software that exploits an innovative algorithm based on hyper-graphs to efficiently identify and graphically represent the dynamics of [Formula: see text]-sheets formation.

The Multi-Level Mechanism of Action of a Pan-Ras Inhibitor Explains its Antiproliferative Activity on Cetuximab-Resistant Cancer Cells.

Ras oncoproteins play a crucial role in the onset, maintenance, and progression of the most common and deadly human cancers. Despite extensive research efforts, only a few mutant-specific Ras inhibitors have been reported. We show that cmp4-previously identified as a water-soluble Ras inhibitor- targets multiple steps in the activation and downstream signaling of different Ras mutants and isoforms.

Surfing on Fitness Landscapes: A Boost on Optimization by Fourier Surrogate Modeling.

Surfing in rough waters is not always as fun as wave riding the "big one". Similarly, in optimization problems, fitness landscapes with a huge number of local optima make the search for the global optimum a hard and generally annoying game. Computational Intelligence optimization metaheuristics use a set of individuals that "surf" across the fitness landscape, sharing and exploiting pieces of information about local fitness values in a joint effort to find out the global optimum.

cuProCell: GPU-Accelerated Analysis of Cell Proliferation With Flow Cytometry Data.

The investigation of cell proliferation can provide useful insights for the comprehension of cancer progression, resistance to chemotherapy and relapse. To this aim, computational methods and experimental measurements based on in vivo label-retaining assays can be coupled to explore the dynamic behavior of tumoral cells. ProCell is a software that exploits flow cytometry data to model and simulate the kinetics of fluorescence loss that is due to stochastic events of cell division.

Fuzzy modeling and global optimization to predict novel therapeutic targets in cancer cells.

Motivation: The elucidation of dysfunctional cellular processes that can induce the onset of a disease is a challenging issue from both the experimental and computational perspectives. Here we introduce a novel computational method based on the coupling between fuzzy logic modeling and a global optimization algorithm, whose aims are to (1) predict the emergent dynamical behaviors of highly heterogeneous systems in unperturbed and perturbed conditions, regardless of the availability of quantitative parameters, and (2) determine a minimal set of system components whose perturbation can lead to a desired system response, therefore facilitating the design of a more appropriate experimental strategy.

Results: We applied this method to investigate what drives K-ras-induced cancer cells, displaying the typical Warburg effect, to death or survival upon progressive glucose depletion.

A novel framework for MR image segmentation and quantification by using MedGA.

Background And Objectives: Image segmentation represents one of the most challenging issues in medical image analysis to distinguish among different adjacent tissues in a body part. In this context, appropriate image pre-processing tools can improve the result accuracy achieved by computer-assisted segmentation methods. Taking into consideration images with a bimodal intensity distribution, image binarization can be used to classify the input pictorial data into two classes, given a threshold intensity value.

GenHap: a novel computational method based on genetic algorithms for haplotype assembly.

Background: In order to fully characterize the genome of an individual, the reconstruction of the two distinct copies of each chromosome, called haplotypes, is essential. The computational problem of inferring the full haplotype of a cell starting from read sequencing data is known as haplotype assembly, and consists in assigning all heterozygous Single Nucleotide Polymorphisms (SNPs) to exactly one of the two chromosomes. Indeed, the knowledge of complete haplotypes is generally more informative than analyzing single SNPs and plays a fundamental role in many medical applications.

Modeling cell proliferation in human acute myeloid leukemia xenografts.

Motivation: Acute myeloid leukemia (AML) is one of the most common hematological malignancies, characterized by high relapse and mortality rates. The inherent intra-tumor heterogeneity in AML is thought to play an important role in disease recurrence and resistance to chemotherapy. Although experimental protocols for cell proliferation studies are well established and widespread, they are not easily applicable to in vivo contexts, and the analysis of related time-series data is often complex to achieve.

GPU-powered model analysis with PySB/cupSODA.

Summary: A major barrier to the practical utilization of large, complex models of biochemical systems is the lack of open-source computational tools to evaluate model behaviors over high-dimensional parameter spaces. This is due to the high computational expense of performing thousands to millions of model simulations required for statistical analysis. To address this need, we have implemented a user-friendly interface between cupSODA, a GPU-powered kinetic simulator, and PySB, a Python-based modeling and simulation framework.

LASSIE: simulating large-scale models of biochemical systems on GPUs.

Background: Mathematical modeling and in silico analysis are widely acknowledged as complementary tools to biological laboratory methods, to achieve a thorough understanding of emergent behaviors of cellular processes in both physiological and perturbed conditions. Though, the simulation of large-scale models-consisting in hundreds or thousands of reactions and molecular species-can rapidly overtake the capabilities of Central Processing Units (CPUs). The purpose of this work is to exploit alternative high-performance computing solutions, such as Graphics Processing Units (GPUs), to allow the investigation of these models at reduced computational costs.

Graphics processing units in bioinformatics, computational biology and systems biology.

Several studies in Bioinformatics, Computational Biology and Systems Biology rely on the definition of physico-chemical or mathematical models of biological systems at different scales and levels of complexity, ranging from the interaction of atoms in single molecules up to genome-wide interaction networks. Traditional computational methods and software tools developed in these research fields share a common trait: they can be computationally demanding on Central Processing Units (CPUs), therefore limiting their applicability in many circumstances. To overcome this issue, general-purpose Graphics Processing Units (GPUs) are gaining an increasing attention by the scientific community, as they can considerably reduce the running time required by standard CPU-based software, and allow more intensive investigations of biological systems.

Computational strategies for a system-level understanding of metabolism.

Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism.

Massive exploration of perturbed conditions of the blood coagulation cascade through GPU parallelization.

The introduction of general-purpose Graphics Processing Units (GPUs) is boosting scientific applications in Bioinformatics, Systems Biology, and Computational Biology. In these fields, the use of high-performance computing solutions is motivated by the need of performing large numbers of in silico analysis to study the behavior of biological systems in different conditions, which necessitate a computing power that usually overtakes the capability of standard desktop computers. In this work we present coagSODA, a CUDA-powered computational tool that was purposely developed for the analysis of a large mechanistic model of the blood coagulation cascade (BCC), defined according to both mass-action kinetics and Hill functions.

cuTauLeaping: a GPU-powered tau-leaping stochastic simulator for massive parallel analyses of biological systems.

Tau-leaping is a stochastic simulation algorithm that efficiently reconstructs the temporal evolution of biological systems, modeled according to the stochastic formulation of chemical kinetics. The analysis of dynamical properties of these systems in physiological and perturbed conditions usually requires the execution of a large number of simulations, leading to high computational costs. Since each simulation can be executed independently from the others, a massive parallelization of tau-leaping can bring to relevant reductions of the overall running time.

In vivo and in silico analysis of PCNA ubiquitylation in the activation of the Post Replication Repair pathway in S. cerevisiae.

Background: The genome of living organisms is constantly exposed to several damaging agents that induce different types of DNA lesions, leading to cellular malfunctioning and onset of many diseases. To maintain genome stability, cells developed various repair and tolerance systems to counteract the effects of DNA damage. Here we focus on Post Replication Repair (PRR), the pathway involved in the bypass of DNA lesions induced by sunlight exposure and UV radiation.

The role of feedback control mechanisms on the establishment of oscillatory regimes in the Ras/cAMP/PKA pathway in S. cerevisiae.

: In the yeast Saccharomyces cerevisiae, the Ras/cAMP/PKA pathway is involved in the regulation of cell growth and proliferation in response to nutritional sensing and stress conditions. The pathway is tightly regulated by multiple feedback loops, exerted by the protein kinase A (PKA) on a few pivotal components of the pathway. In this article, we investigate the dynamics of the second messenger cAMP by performing stochastic simulations and parameter sweep analysis of a mechanistic model of the Ras/cAMP/PKA pathway, to determine the effects that the modulation of these feedback mechanisms has on the establishment of stable oscillatory regimes.

Simulation of the Ras/cAMP/PKA pathway in budding yeast highlights the establishment of stable oscillatory states.

In the yeast Saccharomyces cerevisiae, the Ras/cAMP/PKA pathway plays a major role in the regulation of metabolism, stress resistance and cell cycle progression. We extend here a mechanistic model of the Ras/cAMP/PKA pathway that we previously defined by describing the molecular interactions and post-translational modifications of proteins, and perform a computational analysis to investigate the dynamical behaviors of the components of this pathway, regulated by different control mechanisms. We carry out stochastic simulations to consider, in particular, the effect of the negative feedback loops on the activity of both Ira2 (a Ras-GAP) and Cdc25 (a Ras-GEF) proteins.

Modeling and stochastic simulation of the Ras/cAMP/PKA pathway in the yeast Saccharomyces cerevisiae evidences a key regulatory function for intracellular guanine nucleotides pools.

In the yeast Saccharomyces cerevisiae, the Ras/cAMP/PKA pathway is involved in the regulation of metabolism and cell cycle progression. The pathway is tightly regulated by several control mechanisms, as the feedback cycle ruled by the activity of phosphodiesterase. Here, we present a discrete mathematical model for the Ras/cAMP/PKA pathway that considers its principal cytoplasmic components and their mutual interactions.