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The diagnostic assessment of thyroid nodules is hampered by the persistence of uncertainty in borderline cases, and further complicated by the inclusion of non-invasive follicular tumor with papillary-like nuclear features (NIFTP) as a less aggressive alternative to papillary thyroid carcinoma (PTC). In this setting, computational methods might facilitate the diagnostic process by unmasking key nuclear characteristics of NIFTPs. The main aims of this work were to (1) identify morphometric features of NIFTP and PTC that are interpretable for the human eye, and (2) develop a deep learning model for multi-class segmentation as a support tool to reduce diagnostic variability. Our findings confirmed that nuclei in NIFTP and PTC share multiple characteristics, setting them apart from hyperplastic nodules (HP). The morphometric analysis identified 15 features that can be translated into nuclear alterations readily understandable by pathologists, such as a remarkable inter-nuclear homogeneity for HP in contrast to a major complexity in the chromatin texture of NIFTP, and to the peculiar pattern of nuclear texture variability of PTC. A few NIFTP cases with available NGS data were also analyzed to initially explore the impact of RAS-related mutations on nuclear morphometry. Finally, a pixel-based deep learning model was trained and tested on whole slide images (WSIs) of NIFTP, PTC, and HP cases. The model, named NUTSHELL (NUclei from Thyroid tumors Segmentation to Highlight Encapsulated Low-malignant Lesions), successfully detected and classified the majority of nuclei in all WSIs' tiles, showing comparable results with already well-established pathology nuclear scores. NUTSHELL provides an immediate overview of NIFTP areas and can be used to detect microfoci of PTC within extensive glandular samples or identify lymph node metastases. NUTSHELL can be run inside WSInfer with an easy rendering in QuPath, thus facilitating the democratization of digital pathology.

Several software tools for the simulation and analysis of biochemical reaction networks have been developed in the last decades; however, assessing and comparing their computational performance in executing the typical tasks of computational systems biology can be limited by the lack of a standardized benchmarking approach. To overcome these limitations, we propose here a novel tool, named SMGen, designed to automatically generate synthetic models of reaction networks that, by construction, are characterized by relevant features (e.g., system connectivity and reaction discreteness) and non-trivial emergent dynamics of real biochemical networks. The generation of synthetic models in SMGen is based on the definition of an undirected graph consisting of a single connected component that, generally, results in a computationally demanding task; to speed up the overall process, SMGen exploits a main–worker paradigm. SMGen is also provided with a user-friendly graphical user interface, which allows the user to easily set up all the parameters required to generate a set of synthetic models with any number of reactions and species. We analysed the computational performance of SMGen by generating batches of symmetric and asymmetric reaction-based models (RBMs) of increasing size, showing how a different number of reactions and/or species affects the generation time. Our results show that when the number of reactions is higher than the number of species, SMGen has to identify and correct a large number of errors during the creation process of the RBMs, a circumstance that increases the running time. Still, SMGen can generate synthetic models with hundreds of species and reactions in less than 7 s.

Introduction: 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. However, basecall generally introduces errors that hinder the process of barcode demultiplexing, a pivotal task in single-cell RNA sequencing that allows for separating the sequenced transcripts on the basis of their cell of origin. Methods: To solve this issue, we present a novel framework, called UNPLEX, designed to tackle the barcode demultiplexing problem by operating directly on the recorded signals. UNPLEX combines two unsupervised machine learning methods: autoencoders and self-organizing maps (SOM). The autoencoders extract compact, latent representations of the recorded signals that are then clustered by the SOM. Results and Discussion: Our results, obtained on two datasets composed of in silico generated ONT-like signals, show that UNPLEX represents a promising starting point for the development of effective tools to cluster the signals corresponding to the same cell.

The increasing of experimental observations’ accuracy and model complexity of the heliospheric cosmic rays modulation requires the development of a new class of numerical solvers. In this work, we present a GPU-accelerated code for solving the Parker propagation equation in the heliosphere using a stochastic differential equation (SDE) approach. The presented method uses the CUDA programming language developed for the NVIDIA GPUs. Our approach achieves speedup of the orders of ∼10-40, depending on the number of quasi-particle simulated, compared to the previous CPU implementation. This allows us to efficiently solve the transport equation for the modulated spectra of charged particles in the heliosphere, opening the field for deeper studies and make the realized simulations available for general purpose studies. We demonstrate the accuracy and efficiency of our method through numerical experiments on a realistic model of the heliosphere.

Tumor recognition by T cells is essential for antitumor immunity. A comprehensive characterization of T cell diversity may be key to understanding the success of immunomodulatory drugs and failure of PD-1 blockade in tumors such as multiple myeloma (MM). Here, we use single-cell RNA and T cell receptor sequencing to characterize bone marrow T cells from healthy adults (n = 4) and patients with precursor (n = 8) and full-blown MM (n = 10). Large T cell clones from patients with MM expressed multiple immune checkpoints, suggesting a potentially dysfunctional phenotype. Dual targeting of PD-1 + LAG3 or PD-1 + TIGIT partially restored their function in mice with MM. We identify phenotypic hallmarks of large intratumoral T cell clones, and demonstrate that the CD27− and CD27+ T cell ratio, measured by flow cytometry, may serve as a surrogate of clonal T cell expansions and an independent prognostic factor in 543 patients with MM treated with lenalidomide-based treatment combinations.

Multi-objective optimization algorithms might struggle in finding optimal dominating solutions, especially in real-case scenarios where problems are generally characterized by non-separability, non-differentiability, and multi-modality issues. An effective strategy that already showed to improve the outcome of optimization algorithms consists in manipulating the search space, in order to explore its most promising areas. In this work, starting from a Pareto front identified by an optimization strategy, we exploit Local Bubble Dilation Functions (LBDFs) to manipulate a locally bounded region of the search space containing non-dominated solutions. We tested our approach on the benchmark functions included in the DTLZ and WFG suites, showing that the Pareto front obtained after the application of LBDFs is most of the time characterized by an increased hyper-volume value. Our results confirm that LBDFs are an effective means to identify additional non-dominated solutions that can improve the quality of the Pareto front.

Background and objectives: Myocardial infarction scar (MIS) assessment by cardiac magnetic resonance provides prognostic information and guides patients’ clinical management. However, MIS segmentation is time-consuming and not performed routinely. This study presents a deep-learning-based computational workflow for the segmentation of left ventricular (LV) MIS, for the first time performed on state-of-the-art dark-blood late gadolinium enhancement (DB-LGE) images, and the computation of MIS transmurality and extent. Methods: DB-LGE short-axis images of consecutive patients with myocardial infarction were acquired at 1.5T in two centres between Jan 1, 2019, and June 1, 2021. Two convolutional neural network (CNN) models based on the U-Net architecture were trained to sequentially segment the LV and MIS, by processing an incoming series of DB-LGE images. A 5-fold cross-validation was performed to assess the performance of the models. Model outputs were compared respectively with manual (LV endo- and epicardial border) and semi-automated (MIS, 4-Standard Deviation technique) ground truth to assess the accuracy of the segmentation. An automated post-processing and reporting tool was developed, computing MIS extent (expressed as relative infarcted mass) and transmurality. Results: The dataset included 1355 DB-LGE short-axis images from 144 patients (MIS in 942 images). High performance (> 0.85) as measured by the Intersection over Union metric was obtained for both the LV and MIS segmentations on the training sets. The performance for both LV and MIS segmentations was 0.83 on the test sets. Compared to the 4-Standard Deviation segmentation technique, our system was five times quicker (<1 min versus 7 ± 3 min), and required minimal user interaction. Conclusions: Our solution successfully addresses different issues related to automatic MIS segmentation, including accuracy, time-effectiveness, and the automatic generation of a clinical report.

Artificial intelligence is getting a foothold in medicine for disease screening and diagnosis. While typical machine learning methods require large labeled datasets for training and validation, their application is limited in clinical fields since ground truth information can hardly be obtained on a sizeable cohort of patients. Unsupervised neural networks – such as Self-Organizing Maps (SOMs) – represent an alternative approach to identifying hidden patterns in biomedical data. Here we investigate the feasibility of SOMs for the identification of malignant and non-malignant regions in liquid biopsies of thyroid nodules, on a patient-specific basis. MALDI-ToF (Matrix Assisted Laser Desorption Ionization - Time of Flight) mass spectrometry-imaging (MSI) was used to measure the spectral profile of bioptic samples. SOMs were then applied for the analysis of MALDI-MSI data of individual patients’ samples, also testing various pre-processing and agglomerative clustering methods to investigate their impact on SOMs’ discrimination efficacy. The final clustering was compared against the sample's probability to be malignant, hyperplastic or related to Hashimoto thyroiditis as quantified by multinomial regression with LASSO. Our results show that SOMs are effective in separating the areas of a sample containing benign cells from those containing malignant cells. Moreover, they allow to overlap the different areas of cytological glass slides with the corresponding proteomic profile image, and inspect the specific weight of every cellular component in bioptic samples. We envision that this approach could represent an effective means to assist pathologists in diagnostic tasks, avoiding the need to manually annotate cytological images and the effort in creating labeled datasets.

Combination therapies represent one of the most effective strategy in inducing cancer cell death and reducing the risk to develop drug resistance. The identification of putative novel drug combinations, which typically requires the execution of expensive and time consuming lab experiments, can be supported by the synergistic use of mathematical models and multi-objective optimization algorithms. The computational approach allows to automatically search for potential therapeutic combinations and to test their effectiveness in silico, thus reducing the costs of time and money, and driving the experiments toward the most promising therapies. In this work, we couple dynamic fuzzy modeling of cancer cells with different multi-objective optimization algorithm, and we compare their performance in identifying drug target combinations. Specifically, we perform batches of optimizations with 3 and 4 objective functions defined to achieve a desired behavior of the system (e.g., maximize apoptosis while minimizing necrosis and survival), and we compare the quality of the solutions included in the Pareto fronts. Our results show that both the choice of the multi-objective algorithm and the formulation of the optimization problem have an impact on the identified solutions, highlighting the strengths as well as the limitations of this approach.

Background: Single-cell RNA sequencing (scRNA-Seq) experiments are gaining ground to study the molecular processes that drive normal development as well as the onset of different pathologies. Finding an effective and efficient low-dimensional representation of the data is one of the most important steps in the downstream analysis of scRNA-Seq data, as it could provide a better identification of known or putatively novel cell-types. Another step that still poses a challenge is the integration of different scRNA-Seq datasets. Though standard computational pipelines to gain knowledge from scRNA-Seq data exist, a further improvement could be achieved by means of machine learning approaches. Results: Autoencoders (AEs) have been effectively used to capture the non-linearities among gene interactions of scRNA-Seq data, so that the deployment of AE-based tools might represent the way forward in this context. We introduce here scAEspy, a unifying tool that embodies: (1) four of the most advanced AEs, (2) two novel AEs that we developed on purpose, (3) different loss functions. We show that scAEspy can be coupled with various batch-effect removal tools to integrate data by different scRNA-Seq platforms, in order to better identify the cell-types. We benchmarked scAEspy against the most used batch-effect removal tools, showing that our AE-based strategies outperform the existing solutions. Conclusions: scAEspy is a user-friendly tool that enables using the most recent and promising AEs to analyse scRNA-Seq data by only setting up two user-defined parameters. Thanks to its modularity, scAEspy can be easily extended to accommodate new AEs to further improve the downstream analysis of scRNA-Seq data. Considering the relevant results we achieved, scAEspy can be considered as a starting point to build a more comprehensive toolkit designed to integrate multi single-cell omics.

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 in vitro 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. We tested this approach on a model of oncogenic K-ras cancer cells characterized by a marked Warburg effect. The computational approach was validated by its capability in finding out therapies already known in the literature for this type of cancer cell. More importantly, our results show that this method can suggest potential therapies consisting in a small number of molecular targets. In the model of oncogenic K-ras cancer cells, for instance, we identified combination of up to three targets, which affect different cellular pathways that are crucial for cancer proliferation and survival.

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. ACDC overcomes the limitations of the literature methods, by first applying bilateral filtering on the original image to smooth the input cell images while preserving edge sharpness, and then by exploiting the watershed transform and morphological filtering. Moreover, ACDC represents a feasible solution for the laboratory practice, as it can leverage multi-core architectures in computer clusters to efficiently handle large-scale imaging datasets. Indeed, our Parent-Workers implementation of ACDC allows to obtain up to a 3.7× speed-up compared to the sequential counterpart. ACDC was tested on two distinct cell imaging datasets to assess its accuracy and effectiveness on images with different characteristics. We achieved an accurate cell-count and nuclei segmentation without relying on large-scale annotated datasets, a result confirmed by the average Dice Similarity Coefficients of 76.84 and 88.64 and the Pearson coefficients of 0.99 and 0.96, calculated against the manual cell counting, on the two tested datasets.

Mathematical modeling and computational analyses are essential tools to understand and gain novel insights on the functioning of complex biochemical systems. In the specific case of metabolic reaction networks, which are regulated by many other intracellular processes, various challenging problems hinder the definition of compact and fully calibrated mathematical models, as well as the execution of computationally efficient analyses of their emergent dynamics. These problems especially occur when the model explicitly takes into account the presence and the effect of different isoforms of metabolic enzymes. Since the kinetic characterization of the different isoforms is most of the times unavailable, Parameter Estimation (PE) procedures are typically required to properly calibrate the model. To address these issues, in this work we combine the descriptive power of Stochastic Symmetric Nets, a parametric and compact extension of the Petri Net formalism, with FST-PSO, an efficient and settings-free meta-heuristics for global optimization that is suitable for the PE problem. To prove the effectiveness of our modeling and calibration approach, we investigate here a large-scale kinetic model of human intracellular metabolism. To efficiently execute the large number of simulations required by PE, we exploit LASSIE, a deterministic simulator that offloads the calculations onto the cores of Graphics Processing Units, thus allowing a drastic reduction of the running time. Our results attest that estimating isoform-specific kinetic parameters allows to predict how the knock-down of specific enzyme isoforms affects the dynamic behavior of the metabolic network. Moreover, we show that, thanks to LASSIE, we achieved a speed-up of ~30× with respect to the same analysis carried out on Central Processing Units.

Many researchers have used fuzzy set theory and fuzzy logic in a variety of applications related to computer science and engineering, given the capability of fuzzy inference systems to deal with uncertainty, represent vague concepts, and connect human language to numerical data. In this work we propose Simpful, a general-purpose and user-friendly Python library designed to facilitate the definition, analysis, and interpretation of fuzzy inference systems. Simpful provides a lightweight Application Programming Interface that allows to intuitively define fuzzy sets and fuzzy rules, and to perform fuzzy inference. Worthy of note, in Simpful the fuzzy rules are specified by means of strings of text written in natural language. We provide here some practical examples to show that Simpful represents a valuable addition to the open-source software that supports fuzzy reasoning.

Several mathematical formalisms can be exploited to model complex systems, in order to capture different features of their dynamic behavior and leverage any available quantitative or qualitative data. Correspondingly, either quantitative models or qualitative models can be defined: bridging the gap between these two worlds would allow to simultaneously exploit the peculiar advantages provided by each modeling approach. However, to date attempts in this direction have been limited to specific fields of research. In this work, we propose a novel, general-purpose computational framework, named FuzzX, for the analysis of hybrid models consisting in a quantitative (or mechanistic) module and a qualitative module that can reciprocally control each other's dynamic behavior through a common interface. FuzzX takes advantage of precise quantitative information about the system through the definition and simulation of the mechanistic module. At the same time, it describes the behavior of components and their interaction that are not known in full mechanistic details, by exploiting fuzzy logic for the definition of the qualitative module. We applied FuzzX for the analysis of a hybrid model of a complex biochemical system, characterized by the presence of positive and negative feedback regulations. We show that FuzzX is able to correctly reproduce known emergent behaviors of this system in normal and perturbed conditions. We envision that FuzzX could be employed to analyze any kind of complex system when quantitative information is limited, and to extend existing mechanistic models with fuzzy modules to describe those components and interactions of the system that are not yet fully characterized.

Calcium homeostasis and signaling processes in Saccharomyces cerevisiae, 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. To better understand the role of each channel and transporter involved in the generation and recovery of the calcium transient—and of their feedback regulations—we defined and analyzed a mathematical model of the calcium signaling response to HTS in yeast cells. The model was validated by comparing the simulation outcomes with calcium concentration variations before and during the HTS response, which were observed experimentally in both wild-type and mutant strains. Our results show that calcium normally enters the cell through the High Affinity Calcium influx System and mechanosensitive channels. The increase of the plasma membrane tension, caused by HTS, boosts the opening probability of mechanosensitive channels. This event causes a sudden calcium pulse that is rapidly dissipated by the activity of the vacuolar transporter Pmc1. According to model simulations, the role of another vacuolar transporter, Vcx1, is instead marginal, unless calcineurin is inhibited or removed. Our results also suggest that the mechanosensitive channels are subject to a calcium-dependent feedback inhibition, possibly involving calmodulin. Noteworthy, the model predictions are in accordance with literature results concerning some aspects of calcium homeostasis and signaling that were not specifically addressed within the model itself, suggesting that it actually depicts all the main cellular components and interactions that constitute the HTS calcium pathway, and thus can

Solving optimization problems is one of the most complex and widespread task in Computer Science. In many scenarios, finding the global optimum of a function is hampered by several features that characterize the fitness landscapes, such as noisiness, multi-modality, non-convexity, non-separability, and non-differentiability. In order to facilitate the optimization process, a variety of methods have been proposed to manipulate either the search space or the fitness landscape. Among these, Dilation Functions (DFs) were introduced to expand regions of the search space that are characterized by promising fitness values. In this work, we extend the family of DFs by introducing Local Bubble Dilation Functions (LBDFs), a novel approach that generates local distortions bounded by hyper-spheres. By performing an appropriate mapping of the search space, LBDFs can improve the optimization performance, since they expand and reveal the promising regions around the global optimum, while leaving the rest of the fitness landscape untouched. The additional advantage of LBDFs, with respect to DFs, is that different dilations can be applied to each dimension of the search space, which is useful in the case of asymmetric landscapes. In order to show the benefits of local dilations, we executed several tests on the Michalewicz benchmark function, with different settings for the LBDFs. Our results show that a properly designed LBDF can lead to statistically significant better results than using vanilla optimization. Finally, we investigated the use of LBDFs to facilitate the solution of the parameter estimation problem in Systems Biology by analyzing the landscape related to a stochastic model of enzyme kinetics.

Computational Intelligence (CI) is a computer science discipline encompassing the theory, design, development and application of biologically and linguistically derived computational paradigms. Traditionally, the main elements of CI are Evolutionary Computation, Swarm Intelligence, Fuzzy Logic, and Neural Networks. CI aims at proposing new algorithms able to solve complex computational problems by taking inspiration from natural phenomena. In an intriguing turn of events, these nature-inspired methods have been widely adopted to investigate a plethora of problems related to nature itself. In this paper we present a variety of CI methods applied to three problems in life sciences, highlighting their effectiveness: we describe how protein folding can be faced by exploiting Genetic Programming, the inference of haplotypes can be tackled using Genetic Algorithms, and the estimation of biochemical kinetic parameters can be performed by means of Swarm Intelligence. We show that CI methods can generate very high quality solutions, providing a sound methodology to solve complex optimization problems in life sciences.

Prostate cancer is the most common malignant tumors in men but prostate Magnetic Resonance Imaging (MRI) analysis remains challenging. Besides whole prostate gland segmentation, the capability to differentiate between the blurry boundary of the Central Gland (CG) and Peripheral Zone (PZ) can lead to differential diagnosis, since the frequency and severity of tumors differ in these regions. To tackle the prostate zonal segmentation task, we propose a novel Convolutional Neural Network (CNN), called USE-Net, which incorporates Squeeze-and-Excitation (SE) blocks into U-Net, i.e., one of the most effective CNNs in biomedical image segmentation. Especially, the SE blocks are added after every Encoder (Enc USE-Net) or Encoder-Decoder block (Enc-Dec USE-Net). This study evaluates the generalization ability of CNN-based architectures on three T2-weighted MRI datasets, each one consisting of a different number of patients and heterogeneous image characteristics, collected by different institutions. The following mixed scheme is used for training/testing: (i) training on either each individual dataset or multiple prostate MRI datasets and (ii) testing on all three datasets with all possible training/testing combinations. USE-Net is compared against three state-of-the-art CNN-based architectures (i.e., U-Net, pix2pix, and Mixed-Scale Dense Network), along with a semi-automatic continuous max-flow model. The results show that training on the union of the datasets generally outperforms training on each dataset separately, allowing for both intra-/cross-dataset generalization. Enc USE-Net shows good overall generalization under any training condition, while Enc-Dec USE-Net remarkably outperforms the other methods when trained on all datasets. These findings reveal that the SE blocks’ adaptive feature recalibration provides excellent cross-dataset generalization when testing is performed on samples of the datasets used during training. Therefore, we should consider multi-dataset t

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. Binding of this pan-Ras inhibitor to an extended Switch II pocket on HRas and KRas proteins induces a conformational change that down-regulates intrinsic and GEF-mediated nucleotide dissociation and exchange and effector binding. A mathematical model of the Ras activation cycle predicts that the inhibitor severely reduces the proliferation of different Ras-driven cancer cells, effectively cooperating with Cetuximab to reduce proliferation even of Cetuximab-resistant cancer cell lines. Experimental data confirm the model prediction, indicating that the pan-Ras inhibitor is an appropriate candidate for medicinal chemistry efforts tailored at improving its currently unsatisfactory affinity.

Acute myeloid leukemia (AML) is a highly frequent hematological malignancy, characterized by clinical and biological diversity, along with high relapse and mortality rates. The inherent functional and genetic intra-tumor heterogeneity in AML is thought to play an important role in disease recurrence and resistance to chemotherapy. Patient-derived xenograft (PDX) models preserve important features of the original tumor, allowing, at the same time, experimental manipulation and in vivo amplification of the human cells. Here we present a detailed protocol for the generation of fluorescently labeled AML PDX models to monitor cell proliferation kinetics in vivo, at the single-cell level. 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. To overcome these limitations, model-driven approaches can be exploited to investigate different aspects of cell population dynamics. Among the existing approaches, the ProCell framework is able to perform detailed and accurate stochastic simulations of cell proliferation, relying on flow cytometry data. In particular, by providing an initial and a target fluorescence histogram, ProCell automatically assesses the validity of any user-defined scenario of intra-tumor heterogeneity, that is, it is able to infer the proportion of various cell subpopulations (including quiescent cells) and the division interval of proliferating cells. Here we explain the protocol in detail, providing a description of our methodology for the conditional expression of H2B-GFP in human AML xenografts, data processing by flow cytometry, and the final elaboration in ProCell.

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. On the other hand, the computational analysis of a model typically requires the execution of a large number of simulations for its calibration or to test the effect of perturbations. As a consequence, the computational capabilities of modern Central Processing Units can be easily overtaken, possibly making the modeling of biochemical networks a worthless or ineffective effort. To the aim of overcoming the limitations of the current state-of-the-art simulation approaches, we present in this paper FiCoS, a novel “black-box” deterministic simulator that effectively realizes both a fine-grained and a coarse-grained parallelization on Graphics Processing Units. In particular, FiCoS exploits two different integration methods, namely, the Dormand–Prince and the Radau IIA, to efficiently solve both non-stiff and stiff systems of coupled Ordinary Differential Equations. We tested the performance of FiCoS against different deterministic simulators, by considering models of increasing size and by running analyses with increasing computational demands. FiCoS was able to dramatically speedup the computations up to 855×, showing to be a promising solution for the simulation and analysis of large-scale models of complex biological processes.

Optimization problems represent a class of pervasive and complex tasks in Computer Science, aimed at identifying the global optimum of a given objective function. Optimization problems are typically noisy, multi-modal, non-convex, non-separable, and often non-differentiable. Because of these features, they mandate the use of sophisticated population-based meta-heuristics to effectively explore the search space. Additionally, computational techniques based on the manipulation of the optimization landscape, such as Dilation Functions (DFs), can be effectively exploited to either “compress” or “dilate” some target regions of the search space, in order to improve the exploration and exploitation capabilities of any meta-heuristic. The main limitation of DFs is that they must be tailored on the specific optimization problem under investigation. In this work, we propose a solution to this issue, based on the idea of evolving the DFs. Specifically, we introduce a two-layered evolutionary framework, which combines Evolutionary Computation and Swarm Intelligence to solve the meta-problem of optimizing both the structure and the parameters of DFs. We evolved optimal DFs on a variety of benchmark problems, showing that this approach yields extremely simpler versions of the original optimization problems.

The increasing number of single-cell transcriptomic and single-cell RNA sequencing studies are allowing for a deeper understanding of the molecular processes underlying the normal development of an organism as well as the onset of pathologies. These studies continuously refine the functional roles of known cell populations, and provide their characterization as soon as putatively novel cell populations are detected. In order to isolate the cell populations for further tailored analysis, succinct marker panels-composed of a few cell surface proteins and clusters of differentiation molecules-must be identified. The identification of these marker panels is a challenging computational problem due to its intrinsic combinatorial nature, which makes it an NP-hard problem. Genetic Algorithms (GAs) have been successfully used in Bioinformatics and other biomedical applications to tackle combinatorial problems. We present here a GA-based approach to solve the problem of the identification of succinct marker panels. Since the performance of a GA is strictly related to the representation of the candidate solutions, we propose and compare three alternative representations, able to implicitly introduce different constraints on the search space. For each representation, we perform a fine-tuning of the parameter settings to calibrate the GA, and we show that different representations yield different performance, where the most relaxed representations-in which the GA can also evolve the number of genes in the panel-turn out to be the more effective, especially in the case of 0-knowledge problems. Our results also show that the marker panels identified by GAs can outperform manually curated solutions.

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. In this context, we designed surF, a novel surrogate modeling technique that leverages the discrete Fourier transform to generate a smoother, and possibly easier to explore, fitness landscape. The rationale behind this idea is that filtering out the high frequencies of the fitness function and keeping only its partial information (i.e., the low frequencies) can actually be beneficial in the optimization process. We prove our theory by combining surF with a settings free variant of Particle Swarm Optimization (PSO) based on Fuzzy Logic, called Fuzzy Self-Tuning PSO. Specifically, we introduce a new algorithm, named F3ST-PSO, which performs a preliminary exploration on the surrogate model followed by a second optimization using the actual fitness function. We show that F3ST-PSO can lead to improved performances, notably using the same budget of fitness evaluations.

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. Since the rate of cell division is not known, ProCell embeds a calibration process that might require thousands of stochastic simulations to properly infer the parameterization of cell proliferation models. To mitigate the high computational costs, in this paper we introduce a parallel implementation of ProCell's simulation algorithm, named cuProCell, which leverages Graphics Processing Units (GPUs). Dynamic Parallelism was used to efficiently manage the cell duplication events, in a radically different way with respect to common computing architectures. We present the advantages of cuProCell for the analysis of different models of cell proliferation in Acute Myeloid Leukemia (AML), using data collected from the spleen of human xenografts in mice. We show that, by exploiting GPUs, our method is able to not only automatically infer the models' parameterization, but it is also 237× faster than the sequential implementation. This study highlights the presence of a relevant percentage of quiescent and potentially chemoresistant cells in AML in vivo, and suggests that maintaining a dynamic equilibrium among the different proliferating cell populations might play an important role in disease progression.

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. The optimization analysis allowed to identify new combinations of stimuli that maximize pro-apoptotic processes. Namely, our results provide different evidences of an important protective role for protein kinase A in cancer cells under several cellular stress conditions mimicking tumor behavior. The predictive power of this method could facilitate the assessment of the response of other complex heterogeneous systems to drugs or mutations in fields as medicine and pharmacology, therefore paving the way for the development of novel therapeutic treatments.

FBCA (Flux Balance Cellular Automata) has been recently proposed as a new multi-scale modeling framework to represent the spatial dynamics of multi-cellular systems, while simultaneously taking into account the metabolic activity of individual cells. Preliminary results have revealed the potentialities of the framework in enabling to identify and analyze complex emergent properties of cellular populations, such as spatial patterns phenomena and synchronization effects. Here we move a step forward, by exploring the possibility of integrating real-world data into the framework. To this end, we seek to customize the metabolism of individual cells according to single-cell gene expression profiles. We investigate the effect on cell metabolism of the interplay between: (a) the environmental conditions determined by nutrient diffusion dynamics; (b) the activation or deactivation of metabolic pathways determined by gene expression.

Global optimization problems can be effectively solved by means of Computational Intelligence methods. However, there are several areas in which the effectiveness of these algorithms can be hampered by the computational costs of the fitness evaluations, or by specific features of the fitness landscape that can be characterized by noise and by the presence of several (even infinite) local optima. These issues bring about the necessity of defining specific techniques to replace the original problem with a surrogate representation. Fourier surrogate modeling represents a novel and effective approach to generate smoother, and possibly easier to explore, fitness landscapes, and to reduce the computational effort. Fourier surrogates require an initial sampling of the search space that must be performed to calculate the Fourier transforms. In this paper we investigate the impact on the quality of the surrogate models of the hyper-parameters of the methodology, and of several methods that can be employed for the initial sampling of the fitness landscape (i.e., pseudorandom numbers, low discrepancy sequences, a logistic map in chaotic regime, true random positions generated by a quantum computer, and point packing). Our results show that semistructured approaches like quasi-random sequences and point packing can outperform the other sampling methods.

One of the most complex problems in Systems Biology is Parameter estimation (PE), which consists in inferring the kinetic parameters of biochemical systems. The identification of an accurate parameterization, able to reproduce any observed experimental behavior, is fundamental for the definition of predictive models. PE is a non-convex, multi-modal, and non-separable problem that is usually tackled by using Computational Intelligence methods. When the biochemical species appear in the system in a very low amount, the intrinsic noise due to the randomness of molecular collisions cannot be neglected. In this case, stochastic simulation algorithms should be employed to properly reproduce the system dynamics. Stochastic fluctuations make the PE problem even more complicated, as they can lead to radically different values of the fitness function for the same candidate parameterization. In addition, the kinetic parameters generally follow a log-uniform distribution, so that global optima tend to be localized in the lowest orders of magnitude of the search space. To simultaneously tackle all the aforementioned issues, in this work we investigate a novel approach based on the combination of dilation functions with Fourier surrogate modeling and filtering on the fitness landscape. The results show that our approach is able to strongly simplify the PE problem for low-dimensional optimization instances.

Nowadays, understanding the topology of biological neural networks and sampling their activity is possible thanks to various laboratory protocols that provide a large amount of experimental data, thus paving the way to accurate modeling and simulation. Neuromorphic systems were developed to simulate the dynamics of biological neural networks by means of electronic circuits, offering an efficient alternative to classic simulations based on systems of differential equations, from both the points of view of the energy consumed and the overall computational effort. Spikey is a configurable neuromorphic chip based on the Leaky Integrate-And-Fire model, which gives the user the possibility to model an arbitrary neural topology and simulate the temporal evolution of membrane potentials. To accurately reproduce the behavior of a specific biological network, a detailed parameterization of all neurons in the neuromorphic chip is necessary. Determining such parameters is a hard, error-prone, and generally time consuming task. In this work, we propose a novel methodology for the automatic calibration of neuromorphic chips that exploits a given neural activity as target. Our results show that, in the case of small networks with a low complexity, the method can estimate a vector of parameters capable of reproducing the target activity. Conversely, in the case of more complex networks, the simulations with Spikey can be highly affected by noise, which causes small variations in the simulations outcome even when identical networks are simulated, hindering the convergence to optimal parameterizations.

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. Results: To reconstruct the two haplotypes, we addressed the weighted Minimum Error Correction (wMEC) problem, which is a successful approach for haplotype assembly. This NP-hard problem consists in computing the two haplotypes that partition the sequencing reads into two disjoint sub-sets, with the least number of corrections to the SNP values. To this aim, we propose here GenHap, a novel computational method for haplotype assembly based on Genetic Algorithms, yielding optimal solutions by means of a global search process. In order to evaluate the effectiveness of our approach, we run GenHap on two synthetic (yet realistic) datasets, based on the Roche/454 and PacBio RS II sequencing technologies. We compared the performance of GenHap against HapCol, an efficient state-of-the-art algorithm for haplotype phasing. Our results show that GenHap always obtains high accuracy solutions (in terms of haplotype error rate), and is up to 4× faster than HapCol in the case of Roche/454 instances and up to 20× faster when compared on the PacBio RS II dataset. Finally, we assessed the performance of GenHap on two different real datasets. Conclusions: Future-generation sequencing technologies, producing longer reads with higher coverage, can highly benefit from GenHap, thanks to its capability of efficiently solving large instances of the haplotype assembly problem. Moreover, the optimization approach pr

The reconstruction of the haplotype pair for each chromosome is a hot topic in Bioinformatics and Genome Analysis. In Haplotype Assembly (HA), all heterozygous Single Nucleotide Polymorphisms (SNPs) have to be assigned to exactly one of the two chromosomes. In this work, we outline the state-of-the-art on HA approaches and present an in-depth analysis of the computational performance of GenHap, a recent method based on Genetic Algorithms. GenHap was designed to tackle the computational complexity of the HA problem by means of a divide-et-impera strategy that effectively leverages multi-core architectures. In order to evaluate GenHap’s performance, we generated different instances of synthetic (yet realistic) data exploiting empirical error models of four different sequencing platforms (namely, Illumina NovaSeq, Roche/454, PacBio RS II and Oxford Nanopore Technologies MinION). Our results show that the processing time generally decreases along with the read length, involving a lower number of sub-problems to be distributed on multiple cores.

Computational Intelligence methods, which include Evolutionary Computation and Swarm Intelligence, can efficiently and effectively identify optimal solutions to complex optimization problems by exploiting the cooperative and competitive interplay among their individuals. The exploration and exploitation capabilities of these meta-heuristics are typically assessed by considering well-known suites of benchmark functions, specifically designed for numerical global optimization purposes. However, their performances could drastically change in the case of real-world optimization problems. In this paper, we investigate this issue by considering the Parameter Estimation (PE) of biochemical systems, a common computational problem in the field of Systems Biology. In order to evaluate the effectiveness of various meta-heuristics in solving the PE problem, we compare their performance by considering a set of benchmark functions and a set of synthetic biochemical models characterized by a search space with an increasing number of dimensions. Our results show that some state-of-the-art optimization methods – able to largely outperform the other meta-heuristics on benchmark functions – are characterized by considerably poor performances when applied to the PE problem. We also show that a limiting factor of these optimization methods concerns the representation of the solutions: indeed, by means of a simple semantic transformation, it is possible to turn these algorithms into competitive alternatives. We corroborate this finding by performing the PE of a model of metabolic pathways in red blood cells. Overall, in this work we state that classic benchmark functions cannot be fully representative of all the features that make real-world optimization problems hard to solve. This is the case, in particular, of the PE of biochemical systems. We also show that optimization problems must be carefully analyzed to select an appropriate representation, in order to actually obtain the perfor

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. Unfortunately, adaptive thresholding techniques for two-class segmentation work properly only for images characterized by bimodal histograms. We aim at overcoming these limitations and automatically determining a suitable optimal threshold for bimodal Magnetic Resonance (MR) images, by designing an intelligent image analysis framework tailored to effectively assist the physicians during their decision-making tasks. Methods: In this work, we present a novel evolutionary framework for image enhancement, automatic global thresholding, and segmentation, which is here applied to different clinical scenarios involving bimodal MR image analysis: (i) uterine fibroid segmentation in MR guided Focused Ultrasound Surgery, and (ii) brain metastatic cancer segmentation in neuro-radiosurgery therapy. Our framework exploits MedGA as a pre-processing stage. MedGA is an image enhancement method based on Genetic Algorithms that improves the threshold selection, obtained by the efficient Iterative Optimal Threshold Selection algorithm, between the underlying sub-distributions in a nearly bimodal histogram. Results: The results achieved by the proposed evolutionary framework were quantitatively evaluated, showing that the use of MedGA as a pre-processing stage outperforms the conventional image enhancement methods (i.e., histogram equalization, bi-histogram equalization, Gamma transformation, and sigmoid transformation), in terms of both MR image enhancement and segmentation evalu

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. To overcome these limitations, model-driven approaches can be exploited to investigate different aspects of cell population dynamics. Results: In this work, we present ProCell, a novel modeling and simulation framework to investigate cell proliferation dynamics that, differently from other approaches, takes into account the inherent stochasticity of cell division events. We apply ProCell to compare different models of cell proliferation in AML, notably leveraging experimental data derived from human xenografts in mice. ProCell is coupled with Fuzzy Self-Tuning Particle Swarm Optimization, a swarm-intelligence settings-free algorithm used to automatically infer the models parameterizations. Our results provide new insights on the intricate organization of AML cells with highly heterogeneous proliferative potential, highlighting the important role played by quiescent cells and proliferating cells characterized by different rates of division in the progression and evolution of the disease, thus hinting at the necessity to further characterize tumor cell subpopulations. Availability and implementation: The source code of ProCell and the experimental data used in this work are available under the GPL 2.0 license on GITHUB at the following URL: https://github.com/aresio/ProCell. Supplementary information: Supplementary data are available at Bioinformatics online.

Medical imaging systems often require the application of image enhancement techniques to help physicians in anomaly/abnormality detection and diagnosis, as well as to improve the quality of images that undergo automated image processing. In this work we introduce MedGA, a novel image enhancement method based on Genetic Algorithms that is able to improve the appearance and the visual quality of images characterized by a bimodal gray level intensity histogram, by strengthening their two underlying sub-distributions. MedGA can be exploited as a pre-processing step for the enhancement of images with a nearly bimodal histogram distribution, to improve the results achieved by downstream image processing techniques. As a case study, we use MedGA as a clinical expert system for contrast-enhanced Magnetic Resonance image analysis, considering Magnetic Resonance guided Focused Ultrasound Surgery for uterine fibroids. The performances of MedGA are quantitatively evaluated by means of various image enhancement metrics, and compared against the conventional state-of-the-art image enhancement techniques, namely, histogram equalization, bi-histogram equalization, encoding and decoding Gamma transformations, and sigmoid transformations. We show that MedGA considerably outperforms the other approaches in terms of signal and perceived image quality, while preserving the input mean brightness. MedGA may have a significant impact in real healthcare environments, representing an intelligent solution for Clinical Decision Support Systems in radiology practice for image enhancement, to visually assist physicians during their interactive decision-making tasks, as well as for the improvement of downstream automated processing pipelines in clinically useful measurements.