Omics data are being generated for different conditions, and can be a valuable resource for building novel predictive models for medical diagnosis. Given the reduced number of samples in each dataset, the application of Machine Learning (ML) models requires data integration. At the same time, multiple ML models are available, and the best option for data integration is not known. These challenges have been addressed typically in restricted settings, i.e., for one single disease at a time. However, a thorough comparison of models on integrated data, for different conditions, is still missing. In this paper we confront 7 classifiers on integrated data for 6 diseases, over 14 datasets. We compared the models on single and integrated datasets, employing different pre-processing techniques. We also evaluated the effect of feature selection, analyzing the robustness and relevance of the features extracted. We observed that, even if integration slightly reduces predictive power, the models are still able to produce good classifications. When testing generalization abilities on new datasets, sometimes the performance decreases drastically, depending on the disease studied.

Schizophrenia is a debilitating psychiatric disorder, leading to both physical and social morbidity. Worldwide 1% of the population is struggling with the disease, with 100,000 new cases annually only in the United States. Despite its importance, the goal of finding effective treatments for schizophrenia remains a challenging task, and previous work conducted expensive large-scale phenotypic screens. This work investigates the benefits of Machine Learning for graphs to optimize drug phenotypic screens and predict compounds that mitigate abnormal brain reduction induced by excessive glial phagocytic activity in schizophrenia subjects. Given a compound and its concentration as input, we propose a method that predicts a score associated with three possible compound effects, i.e., reduce, increase, or not influence phagocytosis. We leverage a high-throughput screening to prove experimentally that our method achieves good generalization capabilities. The screening involves 2218 compounds at five different concentrations. Then, we analyze the usability of our approach in a practical setting, i.e., prioritizing the selection of compounds in the SWEETLEAD library. We provide a list of 64 compounds from the library that have the most potential clinical utility for glial phagocytosis mitigation. Lastly, we propose a novel approach to computationally validate their utility as possible therapies for schizophrenia.

MOTIVATION: Precision medicine is a promising field that proposes, in contrast to a one-size-fits-all approach, the tailoring of medical decisions, treatments or products. In this context, it is crucial to introduce innovative methods to stratify a population of patients on the basis of an accurate system-level knowledge of the disease. This is particularly important in very challenging conditions, where the use of standard statistical methods can be prevented by poor data availability or by the need of oversimplifying the processes regulating a complex disease. RESULTS: We define an innovative method for phenotype classification that combines experimental data and a mathematical description of the disease biology. The methodology exploits the mathematical model for inferring additional subject features relevant for the classification. Finally, the algorithm identifies the optimal number of clusters and classifies the samples on the basis of a subset of the features estimated during the model fit. We tested the algorithm in two test cases: an in silico case in the context of dyslipidemia, a complex disease for which a large population of patients has been generated, and a clinical test case, in the context of a lysosomal rare disorder, for which the amount of available data was limited. In both the scenarios, our methodology proved to be accurate and robust, and allowed the inference of an additional phenotype division that the experimental data did not show. AVAILABILITY AND IMPLEMENTATION: The code to reproduce the in silico results has been implemented in MATLAB v.2017b and it is available in the Supplementary Material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

With the recent rising application of mathematical models in the field of computational systems biology, the interest in sensitivity analysis methods had increased. The stochastic approach, based on chemical master equations, and the deterministic approach, based on ordinary differential equations (ODEs), are the two main approaches for analyzing mathematical models of biochemical systems. In this work, the performance of these approaches to compute sensitivity coefficients is explored in situations where stochastic and deterministic simulation can potentially provide different results (systems with unstable steady states, oscillators with population extinction and bistable systems). We consider two methods in the deterministic approach, namely the direct differential method and the finite difference method, and five methods in the stochastic approach, namely the Girsanov transformation, the independent random number method, the common random number method, the coupled finite difference method and the rejection-based finite difference method. The reviewed methods are compared in terms of sensitivity values and computational time to identify differences in outcome that can highlight conditions in which one approach performs better than the other.

With the recent developments in the field of multi-omics integration, the interest in factors such as data preprocessing, choice of the integration method and the number of different omics considered had increased. In this work, the impact of these factors is explored when solving the problem of sample classification, by comparing the performances of five unsupervised algorithms: Multiple Canonical Correlation Analysis, Multiple Co-Inertia Analysis, Multiple Factor Analysis, Joint and Individual Variation Explained and Similarity Network Fusion. These methods were applied to three real data sets taken from literature and several ad hoc simulated scenarios to discuss classification performance in different conditions of noise and signal strength across the data types. The impact of experimental design, feature selection and parameter training has been also evaluated to unravel important conditions that can affect the accuracy of the result.

Exact stochastic simulation is an indispensable tool for a quantitative study of biochemical reaction networks. The simulation realizes the time evolution of the model by randomly choosing a reaction to fire and update the system state according to a probability that is proportional to the reaction propensity. Two computationally expensive tasks in simulating large biochemical networks are the selection of next reaction firings and the update of reaction propensities due to state changes. We present in this work a new exact algorithm to optimize both of these simulation bottlenecks. Our algorithm employs the composition-rejection on the propensity bounds of reactions to select the next reaction firing. The selection of next reaction firings is independent of the number reactions while the update of propensities is skipped and performed only when necessary. It therefore provides a favorable scaling for the computational complexity in simulating large reaction networks. We benchmark our new algorithm with the state of the art algorithms available in literature to demonstrate its applicability and efficiency.

Despite recent progress in proteomics most protein complexes are still unknown. Identification of these complexes will help us understand cellular regulatory mechanisms and support development of new drugs. Therefore it is really important to establish detailed information about the composition and the abundance of protein complexes but existing algorithms can only give qualitative predictions. Herein, we propose a new approach based on stochastic simulations of protein complex formation that integrates multi-source data--such as protein abundances, domain-domain interactions and functional annotations--to predict alternative forms of protein complexes together with their abundances. This method, called SiComPre (Simulation based Complex Prediction), achieves better qualitative prediction of yeast and human protein complexes than existing methods and is the first to predict protein complex abundances. Furthermore, we show that SiComPre can be used to predict complexome changes upon drug treatment with the example of bortezomib. SiComPre is the first method to produce quantitative predictions on the abundance of molecular complexes while performing the best qualitative predictions. With new data on tissue specific protein complexes becoming available SiComPre will be able to predict qualitative and quantitative differences in the complexome in various tissue types and under various conditions.

BACKGROUND: A great deal of data has accumulated on signalling pathways. These large datasets are thought to contain much implicit information on their molecular structure, interaction and activity information, which provides a picture of intricate molecular networks believed to underlie biological functions. While tremendous advances have been made in trying to understand these systems, how information is transmitted within them is still poorly understood. This ever growing amount of data demands we adopt powerful computational techniques that will play a pivotal role in the conversion of mined data to knowledge, and in elucidating the topological and functional properties of protein - protein interactions. RESULTS: A computational framework is presented which allows for the description of embedded networks, and identification of common shared components thought to assist in the transmission of information within the systems studied. By employing the graph theories of network biology - such as degree distribution, clustering coefficient, vertex betweenness and shortest path measures - topological features of protein-protein interactions for published datasets of the p53, nuclear factor kappa B (NF-kappaB) and G1/S phase of the cell cycle systems were ascertained. Highly ranked nodes which in some cases were identified as connecting proteins most likely responsible for propagation of transduction signals across the networks were determined. The functional consequences of these nodes in the context of their network environment were also determined. These findings highlight the usefulness of the framework in identifying possible combination or links as targets for therapeutic responses; and put forward the idea of using retrieved knowledge on the shared components in constructing better organised and structured models of signalling networks. CONCLUSION: It is hoped that through the data mined reconstructed signal transduction networks, well developed models of the published data can be built which in the end would guide the prediction of new targets based on the pathway's environment for further analysis. Source code is available upon request.

Compartments play an important role in molecular and cell biology modeling, which motivated the development of BETA-BINDERS, a formalism which is an extension of the pi-CALCULUS. To execute BETA-BINDERS models, sophisticated simulators are required to ensure a sound and efficient execution. Parallel and distributed simulation represents one means to achieve the later. However, stochastically scheduled events hamper the definition of look aheads for a conservative parallel synchronization scheme, while an optimistic parallel simulation implies expensive rollback operations due to the dynamic structures of BETABINDERS models. Therefore, a time-bounded window approach is suggested, which allows the different logical processes to proceed optimistically up to a barrier. Rollbacks are thus temporally constrained. In addition, the dynamic structure of BETA-BINDERS models requires a special state handling. BETA-BINDERS models and states are represented as tree structures to facilitate state updates and rollbacks by the simulation engine.

We introduce the Beta Workbench (BWB), a scalable tool built on top of the newly defined BlenX language to model, simulate and analyse biological systems. We show the features and the incremental modelling process supported by the BWB on a running example based on the mitogen-activated kinase pathway. Finally, we provide a comparison with related approaches and some hints for future extensions.

A translation of Systems Biology Markup Language (SBML) into a process algebra is proposed in order to allow the formal specification, the simulation and the formal analysis of biological models. Beta-binders, a language with a quantitative stochastic extension, is chosen for the translation. The proposed translation focuses on the main components of SBML models, as species and reactions. Furthermore, it satisfies the compositional property, i.e. the translation of the whole model is obtained by composing the translation of the subcomponents. An automatic translator tool of SBML models into Beta-binders has been implemented as well. Finally, the translation of a simple model is reported.

MOTIVATION: SBML is becoming a standard 'de-facto' to represent and store biological models. Although SBML is very useful in defining ways of exchanging and storing biological information, it is not formal enough to allow direct translation into non ambiguous formal representation languages to perform analysis and simulation of models. We here suggest to map SBML models into process calculi representations. RESULTS: We implemented and validated a tool that translates SBML descriptions into stochastic pi-calculus specifications. AVAILABILITY: Source code is freely available for academic use by contacting the authors.

We introduce a Control Flow Analysis that statically approximates the dynamic behaviour of mobile processes, expressed in (a variant of) the π-calculus. Our analysis of a system is able to describe the essential behaviour of each sub-system, tracking

The need for a more formal handling of biological information processing with stochastic and mobile process algebras is addressed. Biology can benefit this approach, yielding a better understanding of behavioural properties of cells, and computer science can benefit this approach, obtaining new computational models inspired by nature.

We use a structural operational semantics which drives us in inferring quantitative measures on system evolution. The transitions of the system are labeled and we assign rates to them by only looking at these labels. The rates reflect the possibly distributed architecture on which applications run. We then map transition systems to Markov chains, and performance evaluation is carried out using standard tools. As a working example, we compare the performance of a conventional uniprocessor with a prefetch pipeline machine. We also consider two case studies from the literature involving mobile computation to show that our framework is feasible.

We address the problem of message authentication using the /spl pi/-calculus, which has been given an operational semantics that provides each sequential process of a system with its own local space of names. We exploit here that semantics and its localized names to guarantee by construction that a message has been generated by a given entity. Therefore, our proposal can be seen as a reference for the analysis of "real" protocols. As an example, we study the way authentication is ensured by encrypting messages in the spi-calculus.

We extend the π-calculus, a model of concurrent processes based on the notion of naming, to cope with performance modelling. The new language is called the stochastic π-calculus (Sπ). We obtain a more expressive language than classical stochastic process algebras because Sπ is given in the classical structural operational approach. In order to effectively computer performance measures, we define a stratified transition system that is finitely branching. We give a transition rule to directly yield a continuous time Markov chain from an Sπ specification, with no transition system manipulation.

Abstract Error recovery techniques for LR parsers presented in the literature are described and classified. The techniques considered range from the non‐correcting ones to interactive and incremental ones. Also, some of the techniques presented are compared and evaluated. An example showing the advantages and the disadvantages of each class of strategies is given and is used as a guideline for classifying syntax errors according to the recovery strategies which are more adequate to correct them.

Abstract There can be different views of a concurrent distributed system, depending on who observes it. The final user may just want to know how the system behaves in terms of its possible sequences of actions, while the designer may want to know what are the sequential components of a system or how are they distributed in space. In other words, there is not a widely accepted single semantic model for concurrent systems. This paper describes a parametric verification tool for process description languages. It performs a symbolic execution of processes at different levels of abstraction and verifies deadlock and reachability properties. The tool also provides facilities to check behavioural equivalences. In addition to the classical interleaving semantics, truly concurrent approaches based on the notion of causality and locality are considered. This allows us to compare the expressive power of different models within the same environment. In this respect, we have verified many of the examples given in the literature using our tool.