Abstract Aim Acute myeloid leukemia (AML) is a heterogeneous malignancy caused by the proliferation of neoplastic myeloid progenitor cells. With mutations in TET2 playing a critical role in leukemogenesis and therapeutic response. Population-specific mutational patterns remain largely unexplored. Multiple sequence alignment and phylogenetic analysis was used to elucidate the evolutionary conservation and divergence of TET2 sequences in Pakistani AML patients providing insights into mutational hotspots and population-specific patterns. Methods Twenty-five TET2 gene sequences (12 AML and 13 controls) from the Pakistani population were analyzed using multiple sequence alignment, distance matrix analysis, and phylogenetic tree construction using Geneious software (https://www.geneious.com/). Results AML samples showed significant deletions in the catalytic domain (positions 693–876), essential for enzymatic function, and showed reduced sequence conservation than controls. Phylogenetic analysis revealed a distinct separation between AML and control clusters with high bootstrap values, indicating clear evolutionary divergence. Conclusions Our findings revealed distinct TET2 mutational hotspots and evolutionary patterns that may inform biomarker development and targeted therapy strategies. These results motivate large-scale, diverse-population research to unravel the global effects of TET2 variations, and support the use of phylogenetics analysis in precision oncology. References 1. Q. Gao, K. Shen, and M. Xiao, ‘TET2 mutation in acute myeloid leukemia: biology, clinical significance, and therapeutic insights,’ Dec. 01, 2024. doi: 10.1186/s13148-024-01771-2. 2. Han, J. A., An, J., & Ko, M. (2015). Functions of TET proteins in hematopoietic transformation. Molecules and cells, 38(11), 925–935. 3. Schwartz, R., & Schäffer, A. A. (2017). The evolution of tumour phylogenetics: principles and practice. Nature Reviews Genetics, 18(4), 213–229.

MOTIVATION: Single-cell profiling has become a common practice to investigate the complexity of tissues, organs, and organisms. Recent technological advances are expanding our capabilities to profile various molecular layers beyond the transcriptome such as, but not limited to, the genome, the epigenome, and the proteome. Depending on the experimental procedure, these data can be obtained from separate assays or the very same cells. Yet, integration of more than two assays is currently not supported by the majority of the computational frameworks avaiable. RESULTS: We here propose a Multi-Omic data integration framework based on Wasserstein Generative Adversarial Networks suitable for the analysis of paired or unpaired data with a high number of modalities (>2). At the core of our strategy is a single network trained on all modalities together, limiting the computational burden when many molecular layers are evaluated. AVAILABILITY AND IMPLEMENTATION: Source code of our framework is available at https://github.com/vgiansanti/MOWGAN.

BACKGROUND: Longitudinal single-cell sequencing experiments of patient-derived models are increasingly employed to investigate cancer evolution. In this context, robust computational methods are needed to properly exploit the mutational profiles of single cells generated via variant calling, in order to reconstruct the evolutionary history of a tumor and characterize the impact of therapeutic strategies, such as the administration of drugs. To this end, we have recently developed the LACE framework for the Longitudinal Analysis of Cancer Evolution. RESULTS: The LACE 2.0 release aimed at inferring longitudinal clonal trees enhances the original framework with new key functionalities: an improved data management for preprocessing of standard variant calling data, a reworked inference engine, and direct connection to public databases. CONCLUSIONS: All of this is accessible through a new and interactive Shiny R graphical interface offering the possibility to apply filters helpful in discriminating relevant or potential driver mutations, set up inferential parameters, and visualize the results. The software is available at: github.com/BIMIB-DISCo/LACE.

A key challenge in computer vision and deep learning is the definition of robust strategies for the detection of adversarial examples. In this work, we propose the adoption of ensemble approaches to leverage the effectiveness of multiple detectors in exploiting distinct properties of the input data. To this end, the ENsemble Adversarial Detector (ENAD) framework integrates scoring functions from state-of-the-art detectors based on Mahalanobis distance, Local Intrinsic Dimensionality, and One-Class Support Vector Machines, which process the hidden features of deep neural networks. ENAD is designed to ensure high standardization and reproducibility to the computational workflow. Extensive tests on benchmark datasets, models and adversarial attacks show that ENAD outperforms all competing methods in the large majority of settings. The improvement over the state-of-the-art and the intrinsic generality of the framework, which allows one to easily extend ENAD to include any set of detectors and integration strategies, set the foundations for the new area of ensemble adversarial detection.

Single-cell RNA and ATAC sequencing technologies enable the examination of gene expression and chromatin accessibility in individual cells, providing insights into cellular phenotypes. In cancer research, it is important to consistently analyze these states within an evolutionary context on genetic clones. Here we present CONGAS+, a Bayesian model to map single-cell RNA and ATAC profiles onto the latent space of copy number clones. CONGAS+ clusters cells into tumour subclones with similar ploidy, rendering straightforward to compare their expression and chromatin profiles. The framework, implemented on GPU and tested on real and simulated data, scales to analyse seamlessly thousands of cells, demonstrating better performance than single-molecule models, and supporting new multi-omics assays. In prostate cancer, lymphoma and basal cell carcinoma, CONGAS+ successfully identifies complex subclonal architectures while providing a coherent mapping between ATAC and RNA, facilitating the study of genotype-phenotype maps and their connection to genomic instability.

BACKGROUND: The combined effects of biological variability and measurement-related errors on cancer sequencing data remain largely unexplored. However, the spatio-temporal simulation of multi-cellular systems provides a powerful instrument to address this issue. In particular, efficient algorithmic frameworks are needed to overcome the harsh trade-off between scalability and expressivity, so to allow one to simulate both realistic cancer evolution scenarios and the related sequencing experiments, which can then be used to benchmark downstream bioinformatics methods. RESULT: We introduce a Julia package for SPAtial Cancer Evolution (J-SPACE), which allows one to model and simulate a broad set of experimental scenarios, phenomenological rules and sequencing settings.Specifically, J-SPACE simulates the spatial dynamics of cells as a continuous-time multi-type birth-death stochastic process on a arbitrary graph, employing different rules of interaction and an optimised Gillespie algorithm. The evolutionary dynamics of genomic alterations (single-nucleotide variants and indels) is simulated either under the Infinite Sites Assumption or several different substitution models, including one based on mutational signatures. After mimicking the spatial sampling of tumour cells, J-SPACE returns the related phylogenetic model, and allows one to generate synthetic reads from several Next-Generation Sequencing (NGS) platforms, via the ART read simulator. The results are finally returned in standard FASTA, FASTQ, SAM, ALN and Newick file formats. CONCLUSION: J-SPACE is designed to efficiently simulate the heterogeneous behaviour of a large number of cancer cells and produces a rich set of outputs. Our framework is useful to investigate the emergent spatial dynamics of cancer subpopulations, as well as to assess the impact of incomplete sampling and of experiment-specific errors. Importantly, the output of J-SPACE is designed to allow the performance assessment of downstream bioinformatics pipelines processing NGS data. J-SPACE is freely available at: https://github.com/BIMIB-DISCo/J-Space.jl .

A current challenge in cancer research is the development of therapeutic strategies aimed at reducing the toxicity of treatments, since Adverse Events (AEs) typically cause substantial problems and long-term damages to the patients. A possible solution to this issue lies in the personalization of therapy dosages according to demographic factors and in the employment of optimized data-driven drug administration protocols. Control theory can be exploited to this end, as its application in pharmacology allows to define optimized dosages and schedules, aimed at minimizing AEs and maximizing the therapy efficacy. However, an effective application of control theory approaches to this issue is constrained by our ability in inferring the parameters of the mathematical models from currently available data.We here present a closed-loop optimization framework of patient-specific pharmacokinetics (PK) and pharmacodynamics (PD) models, combined with a mathematical model of a liquid tumor, which aims at overcoming such limitations. The most relevant feature of our framework is the ability to learn the value of patient-specific parameters via a Bayesian update, by exploiting a feedback signal obtained monitoring the tumor burden dynamics of the patient. Our framework employs CasADi, an open-source tool for nonlinear optimization, and guarantees a good and robust numerical estimation of the optimized schedule and a parsimonious use of computational time.As a case study, we present the application of our framework to Tyrosine Kinase Inhibitor administration in Chronic Myeloid Leukemia (CML), in which we show that our optimized protocols result in a faster decay of CSCs and in a reduction of the overall toxicity.

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.

The metabolic processes related to the synthesis of the molecules needed for a new round of cell division underlie the complex behaviour of cell populations in multi-cellular systems, such as tissues and organs, whereas their deregulation can lead to pathological states, such as cancer. Even within genetically homogeneous populations, complex dynamics, such as population oscillations or the emergence of specific metabolic and/or proliferative patterns, may arise, and this aspect is highly amplified in systems characterized by extreme heterogeneity. To investigate the conditions and mechanisms that link metabolic processes to cell population dynamics, we here employ a previously introduced multi-scale model of multi-cellular system, named FBCA (Flux Balance Analysis with Cellular Automata), which couples biomass accumulation, simulated via Flux Balance Analysis of a metabolic network, with the simulation of population and spatial dynamics via Cellular Potts Models. In this work, we investigate the influence that different modes of nutrients diffusion within the system may have on the emerging behaviour of cell populations. In our model, metabolic communication among cells is allowed by letting secreted metabolites to diffuse over the lattice, in addition to diffusion of nutrients from given sources. The inclusion of the diffusion processes in the model proved its effectiveness in characterizing plausible biological scenarios.

BACKGROUND: A large number of algorithms is being developed to reconstruct evolutionary models of individual tumours from genome sequencing data. Most methods can analyze multiple samples collected either through bulk multi-region sequencing experiments or the sequencing of individual cancer cells. However, rarely the same method can support both data types. RESULTS: We introduce TRaIT, a computational framework to infer mutational graphs that model the accumulation of multiple types of somatic alterations driving tumour evolution. Compared to other tools, TRaIT supports multi-region and single-cell sequencing data within the same statistical framework, and delivers expressive models that capture many complex evolutionary phenomena. TRaIT improves accuracy, robustness to data-specific errors and computational complexity compared to competing methods. CONCLUSIONS: We show that the application of TRaIT to single-cell and multi-region cancer datasets can produce accurate and reliable models of single-tumour evolution, quantify the extent of intra-tumour heterogeneity and generate new testable experimental hypotheses.

The emergence and development of cancer is a consequence of the accumulation over time of genomic mutations involving a specific set of genes, which provides the cancer clones with a functional selective advantage. In this work, we model the order of accumulation of such mutations during the progression, which eventually leads to the disease, by means of probabilistic graphic models, i.e., Bayesian Networks (BNs). We investigate how to perform the task of learning the structure of such BNs, according to experimental evidence, adopting a global optimization meta-heuristics. In particular, in this work we rely on Genetic Algorithms, and to strongly reduce the execution time of the inference-which can also involve multiple repetitions to collect statistically significant assessments of the data-we distribute the calculations using both multi-threading and a multi-node architecture. The results show that our approach is characterized by good accuracy and specificity; we also demonstrate its feasibility, thanks to a 84× reduction of the overall execution time with respect to a traditional sequential implementation.

MOTIVATION: We introduce TRanslational ONCOlogy (TRONCO), an open-source R package that implements the state-of-the-art algorithms for the inference of cancer progression models from (epi)genomic mutational profiles. TRONCO can be used to extract population-level models describing the trends of accumulation of alterations in a cohort of cross-sectional samples, e.g. retrieved from publicly available databases, and individual-level models that reveal the clonal evolutionary history in single cancer patients, when multiple samples, e.g. multiple biopsies or single-cell sequencing data, are available. The resulting models can provide key hints for uncovering the evolutionary trajectories of cancer, especially for precision medicine or personalized therapy. AVAILABILITY AND IMPLEMENTATION: TRONCO is released under the GPL license, is hosted at http://bimib.disco.unimib.it/ (Software section) and archived also at bioconductor.org. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Dynamical models of gene regulatory networks (GRNs) are highly effective in describing complex biological phenomena and processes, such as cell differentiation and cancer development. Yet, the topological and functional characterization of real GRNs is often still partial and an exhaustive picture of their functioning is missing. RESULTS: We here introduce CABERNET, a Cytoscape app for the generation, simulation and analysis of Boolean models of GRNs, specifically focused on their augmentation when a only partial topological and functional characterization of the network is available. By generating large ensembles of networks in which user-defined entities and relations are added to the original core, CABERNET allows to formulate hypotheses on the missing portions of real networks, as well to investigate their generic properties, in the spirit of complexity science. CONCLUSIONS: CABERNET offers a series of innovative simulation and modeling functions and tools, including (but not being limited to) the dynamical characterization of the gene activation patterns ruling cell types and differentiation fates, and sophisticated robustness assessments, as in the case of gene knockouts. The integration within the widely used Cytoscape framework for the visualization and analysis of biological networks, makes CABERNET a new essential instrument for both the bioinformatician and the computational biologist, as well as a computational support for the experimentalist. An example application concerning the analysis of an augmented T-helper cell GRN is provided.

UNLABELLED: We devise a novel inference algorithm to effectively solve the cancer progression model reconstruction problem. Our empirical analysis of the accuracy and convergence rate of our algorithm, CAncer PRogression Inference (CAPRI), shows that it outperforms the state-of-the-art algorithms addressing similar problems. MOTIVATION: Several cancer-related genomic data have become available (e.g. The Cancer Genome Atlas, TCGA) typically involving hundreds of patients. At present, most of these data are aggregated in a cross-sectional fashion providing all measurements at the time of diagnosis. Our goal is to infer cancer 'progression' models from such data. These models are represented as directed acyclic graphs (DAGs) of collections of 'selectivity' relations, where a mutation in a gene A 'selects' for a later mutation in a gene B. Gaining insight into the structure of such progressions has the potential to improve both the stratification of patients and personalized therapy choices. RESULTS: The CAPRI algorithm relies on a scoring method based on a probabilistic theory developed by Suppes, coupled with bootstrap and maximum likelihood inference. The resulting algorithm is efficient, achieves high accuracy and has good complexity, also, in terms of convergence properties. CAPRI performs especially well in the presence of noise in the data, and with limited sample sizes. Moreover CAPRI, in contrast to other approaches, robustly reconstructs different types of confluent trajectories despite irregularities in the data. We also report on an ongoing investigation using CAPRI to study atypical Chronic Myeloid Leukemia, in which we uncovered non trivial selectivity relations and exclusivity patterns among key genomic events. AVAILABILITY AND IMPLEMENTATION: CAPRI is part of the TRanslational ONCOlogy R package and is freely available on the web at: http://bimib.disco.unimib.it/index.php/Tronco CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Mathematical and computational modelling of biochemical systems has seen a lot of effort devoted to the definition and implementation of high-performance mechanistic simulation frameworks. Within these frameworks it is possible to analyse complex models under a variety of configurations, eventually selecting the best setting of, e.g., parameters for a target system. MOTIVATION: This operational pipeline relies on the ability to interpret the predictions of a model, often represented as simulation time-series. Thus, an efficient data analysis pipeline is crucial to automatise time-series analyses, bearing in mind that errors in this phase might mislead the modeller's conclusions. RESULTS: For this reason we have developed an intuitive framework-independent Python tool to automate analyses common to a variety of modelling approaches. These include assessment of useful non-trivial statistics for simulation ensembles, e.g., estimation of master equations. Intuitive and domain-independent batch scripts will allow the researcher to automatically prepare reports, thus speeding up the usual model-definition, testing and refinement pipeline.

SUMMARY: The characterization of the complex phenomenon of cell differentiation is a key goal of both systems and computational biology. GeStoDifferent is a Cytoscape plugin aimed at the generation and the identification of gene regulatory networks (GRNs) describing an arbitrary stochastic cell differentiation process. The (dynamical) model adopted to describe general GRNs is that of noisy random Boolean networks (NRBNs), with a specific focus on their emergent dynamical behavior. GeStoDifferent explores the space of GRNs by filtering the NRBN instances inconsistent with a stochastic lineage differentiation tree representing the cell lineages that can be obtained by following the fate of a stem cell descendant. Matched networks can then be analyzed by Cytoscape network analysis algorithms or, for instance, used to define (multiscale) models of cellular dynamics. AVAILABILITY: Freely available at http://bimib.disco.unimib.it/index.php/Retronet#GESTODifferent or at the Cytoscape App Store http://apps.cytoscape.org/.

"Signal" alignments play critical roles in many clinical setting. This is the case of mass spectrometry data, an important component of many types of proteomic analysis. A central problem occurs when one needs to integrate (mass spectrometry) data produced by different sources, e.g., different equipment and/or laboratories. In these cases some form of "data integration'" or "data fusion'" may be necessary in order to discard some source specific aspects and improve the ability to perform a classification task such as inferring the "disease classes'" of patients. The need for new high performance data alignments methods is therefore particularly important in these contexts. In this paper we propose an approach based both on an information theory perspective, generally used in a feature construction problem, and on the application of a mathematical programming task (i.e. the weighted bipartite matching problem). We present the results of a competitive analysis of our method against other approaches. The analysis was conducted on data from plasma/ethylenediaminetetraacetic acid (EDTA) of "control" and Alzheimer patients collected from three different hospitals. The results point to a significant performance advantage of our method with respect to the competing ones tested.

ajor advances in genome science and molecular technologies provide new opportunities at the interface between basic biological research and medical practice.The unprecedented completeness, accuracy, and volume of genomic and molecular data necessitate a new kind of computational biology for translational research.Key challenges are standardization of data capture and communication, organization of easily accessible repositories, and algorithms for integrated analysis based on heterogeneous sources of information.Also required are new ways of using complementary clinical and biological data, such as computational methods for predicting disease phenotype from molecular and genetic profiling.New combined experimental and computational methods hold the promise of more accurate diagnosis and prognosis as well as more effective prevention and therapy.

We address the problem of the synthesis of controller programs for a variety of robotics and manufacturing tasks. The problem we choose for the test and illustrative purposes is the standard "walking machine problem", a representative instance of a real hybrid problem with both logical/discrete and continuous properties and strong mutual influence without any reasonable separation. We aim to produce a "compiler technology" for this class of problems in a manner analogous to the development of the so-called "silicon compilers" for the VLSI technology. To cope with the difficulties inherent to the problem, we resort to a novel approach that combines many key ideas from a variety of disciplines, namely discrete event supervisory systems, Petri nets approaches and temporal logic.