Despite the intensive use of radiotherapy in clinical practice, its effectiveness depends on several factors. Several studies showed that the tumour response to radiation differs from one patient to another. The non-uniform response of the tumour is mainly caused by multiple interactions between the tumour microenvironment and healthy cells. To understand these interactions, five major biologic concepts called the "5 Rs" have emerged. These concepts include reoxygenation, DNA damage repair, cell cycle redistribution, cellular radiosensitivity and cellular repopulation. In this study, we used a multi-scale model, which included the five Rs of radiotherapy, to predict the effects of radiation on tumour growth. In this model, the oxygen level was varied in both time and space. When radiotherapy was given, the sensitivity of cells depending on their location in the cell cycle was taken in account. This model also considered the repair of cells by giving a different probability of survival after radiation for tumour and normal cells. Here, we developed four fractionation protocol schemes. We used simulated and positron emission tomography (PET) imaging with the hypoxia tracer 18F-flortanidazole (18F-HX4) images as input data of our model. In addition, tumour control probability curves were simulated. The result showed the evolution of tumours and normal cells. The increase in the cell number after radiation was seen in both normal and malignant cells, which proves that repopulation was included in this model. The proposed model predicts the tumour response to radiation and forms the basis for a more patient-specific clinical tool where related biological data will be included.

A hybrid model is proposed in this study to predict rectal tumour response during radiotherapy treatment. As the oxygen partial pressure distribution (pO2) is a data which is naturally represented at the microscopic scale, we firstly estimate the optimal pO2 distribution using both a diffusion equation and a discrete multi-scale model (that we proposed in a previous study). The aim is to use the effectiveness in algorithmic complexity of the discrete model and its multi-scale aspect in this work to estimate biological information at cellular scale and then construct them at macroscopic scale. Secondly, the obtained pO2 distribution results are used as an input of a biomechanical model in order to simulate tumour volume evolution during radiotherapy. FDG PET images of 21 rectal cancer patients undergoing radiotherapy are used to simulate the tumour evolution during the treatment. The simulated results using the proposed hybride model, allow the interpretation of tumour aggressiveness.

OBJECTIVES:Manual systematic literature reviews are becoming increasingly challenging due to the sharp rise in publications. The primary objective of this literature review was to compare manual and computer software using artificial intelligence retrieval of publications on the cutaneous manifestations of primary SS, but we also evaluated the prevalence of cutaneous manifestations in primary SS. METHODS:We compared manual searching and searching with the in-house computer software BIbliography BOT (BIBOT) designed for article retrieval and analysis. Both methods were used for a systematic literature review on a complex topic, i.e. the cutaneous manifestations of primary SS. Reproducibility was estimated by computing Cohen's κ coefficients and was interpreted as follows: slight, 0-0.20; fair, 0.21-0.40; moderate, 0.41-0.60; substantial, 0.61-0.80; and almost perfect, 0.81-1. RESULTS:The manual search retrieved 855 articles and BIBOT 1042 articles. In all, 202 articles were then selected by applying exclusion criteria. Among them, 155 were retrieved by both methods, 33 by manual search only, and 14 by BIBOT only. Reliability (κ = 0.84) was almost perfect. Further selection was performed by reading the 202 articles. Cohort sizes and the nature and prevalence of cutaneous manifestations varied across publications. In all, we found 52 cutaneous manifestations reported in primary SS patients. The most described ones were cutaneous vasculitis (561 patients), xerosis (651 patients) and annular erythema (215 patients). CONCLUSION:Among the final selection of 202 articles, 155/202 (77%) were found by the two methods but BIBOT was faster and automatically classified the articles in a chart. Combining the two methods retrieved the largest number of publications.

Hemophilia is a rare hemorrhagic disorder caused by clotting factor deficiencies that leads to a less efficient coagulation system. Treatments of this pathology rely on a patient’s subjective assessment which reflects a need for a laboratory assay able to predict the clinical patient phenotype. According to the literature, global assays such as thrombin generation (TG), are good predictors of bleeding episodes and therefore seem to be good candidates to fit this need. Nevertheless, the result of the TG assay, known as thrombogram, is difficult to interpret for non-expert clinicians. In this paper, we present a machine learning-based clinical decision support system which goal is to help clinical decision making. In doing so, we have adopted several approaches in order to evaluate well-known machine learning algorithms, in terms of accuracy and robustness, on a thrombogram database generated using numerical simulations. Obtained results, 95.57% of accuracy using a cascade of a SVM and MLPs to classify all categories and 98.10% of accuracy for the binary case hemophilia A/B, prove that our proposal can efficiently diagnose hemophilia.

ABSTRACT Big data analysis has become a common way to extract information from complex and large datasets among most scientific domains. This approach is now used to study large cohorts of patients in medicine. This work is a review of publications that have used artificial intelligence and advanced machine learning techniques to study physio pathogenesis-based treatments in pSS. A systematic literature review retrieved all articles reporting on the use of advanced statistical analysis applied to the study of systemic autoimmune diseases (SADs) over the last decade. An automatic bibliography screening method has been developed to perform this task. The program called BIBOT was designed to fetch and analyze articles from the pubmed database using a list of keywords and Natural Language Processing approaches. The evolution of trends in statistical approaches, sizes of cohorts and number of publications over this period were also computed in the process. In all, 44077 abstracts were screened and 1017 publications were analyzed. The mean number of selected articles was 101.0 (S.D. 19.16) by year, but increased significantly over the time (from 74 articles in 2008 to 138 in 2017). Among them only 12 focused on pSS but none of them emphasized on the aspect of pathogenesis-based treatments. To conclude, medicine progressively enters the era of big data analysis and artificial intelligence, but these approaches are not yet used to describe pSS-specific pathogenesis-based treatment. Nevertheless, large multicentre studies are investigating this aspect with advanced algorithmic tools on large cohorts of SADs patients.

Big data analysis has become a common way to extract information from complex and large datasets among most scientific domains. This approach is now used to study large cohorts of patients in medicine. This work is a review of publications that have used artificial intelligence and advanced machine learning techniques to study physio pathogenesis-based treatments in pSS. A systematic literature review retrieved all articles reporting on the use of advanced statistical analysis applied to the study of systemic autoimmune diseases (SADs) over the last decade. An automatic bibliography screening method has been developed to perform this task. The program called BIBOT was designed to fetch and analyze articles from the pubmed database using a list of keywords and Natural Language Processing approaches. The evolution of trends in statistical approaches, sizes of cohorts and number of publications over this period were also computed in the process. In all, 44077 abstracts were screened and 1017 publications were analyzed. The mean number of selected articles was 101.0 (S.D. 19.16) by year, but increased significantly over the time (from 74 articles in 2008 to 138 in 2017). Among them only 12 focused on pSS but none of them emphasized on the aspect of pathogenesis-based treatments. To conclude, medicine progressively enters the era of big data analysis and artificial intelligence, but these approaches are not yet used to describe pSS-specific pathogenesis-based treatment. Nevertheless, large multicentre studies are investigating this aspect with advanced algorithmic tools on large cohorts of SADs patients.

We present a multi-scale approach of tumor modeling in order to predict its evolution during radiotherapy. Within this context we focus on three different scales of tumor modeling: microscopic (individual cells in a voxel), mesoscopic (population of cells in a voxel) and macroscopic (whole tumor), with transition interfaces between these three scales. At the cellular level, the description is based on phase transfer probabilities in the cellular cycle. At the mesoscopic scale we represent populations of cells according to different stages in a cell cycle. Finally, at the macroscopic scale, the tumor description is based on the use of FDG PET image voxels. These three scales exist naturally: biological data are collected at the macroscopic scale, but the pathological behavior of the tumor is based on an abnormal cell-cycle at the microscopic scale. On the other hand, the introduction of a mesoscopic scale is essential in order to reduce the gap between the two extreme, in terms of resolution, description levels. It also reduces the computational burden of simulating a large number of individual cells. As an application of the proposed multi-scale model, we simulate the effect of oxygen on tumor evolution during radiotherapy. Two consecutive FDG PET images of 17 rectal cancer patients undergoing radiotherapy are used to simulate the tumor evolution during treatment. The simulated results are compared with those obtained on a third FDG PET image acquired two weeks after the beginning of the treatment.

The classical concept of synchronization is usually related to the locking of the basic frequencies and instantaneous phases of regular oscillations, and this question is addressed by studying specific kinds of coupled systems. This work presents a different point of view. We do not study the convergence of coupled systems to a synchronized behaviour, but try to answer the following question: in a population of coupled differential systems, when each cell (subsystem) exhibits a periodic behaviour, is the whole trajectory periodic? We define generalized spatial automata, with reference to continuous spatial automata, by means of coupling maps and associated measures on the set of cells: the main idea is the fact that a cell interprets its own environment via the states of the whole population and according to its own state. A natural partition of periods is such that cells belong to the same class if their trajectories share a common period. We demonstrate that in a general case where cells belong to an a priori unstructured set, and their trajectories evolve in possibly distinct Banach spaces, the set of classes of periods is generally countable. In particular, when the set of cells is endowed with a Borelian structure, all the cells necessarily share a common period.

By means of an interdisciplinary collaboration, we build a three dimensional individual-based model at the microscopic scale. This model is based on the cardinal model at the population scale, and aims at investigating the impact of spatialisation on the growth of bacterial colonies, in particular in the case of species in interaction. Our case of application is the influence of lactic acid bacteria on pathogens, which growth depends on pH evolution from lactic acid production and diffusion, according to carbohydrate concentration, temperature, water activity and ratio of both populations. We use our individual-based model to study and illustrate different and major effects of spatialisation on colonies growth. The last section presents some perspectives for the design of a continuous partial derivative equations-based companion model to our individual-based one.

By interacting with pathogens, lactic acid bacteria (LAB) are able to contribute to food safety. By means of their lactic acid production which induces pH decrease, LAB influence the growth of pathogens. The aim of this study is to model and simulate lactic acid production, pH evolution, according to carbohydrate concentration in media, temperature, water activity and ratio of both population.

This work takes place in the context of biochemical kinetics simulation for the understanding of complex biological systems such as hæmostasis. The classical approach, based on the numerical solving of differential systems, cannot satisfactorily handle local geometrical constraints, such as membrane binding events. To address this problem, we propose a particle-based system in which each molecular species is represented by a three-dimensional entity which diffuses and may undergo reactions. Such a system can be computationaly intensive, since a small time step and a very large number of entities are required to get significant results. Therefore, we propose a model that is suitable for parallel computing and that can especially take advantage of recent multicore and multiprocessor architectures. We present our particle-based system, detail the behaviour of our entities, and describe our parallel computing algorithms. Comparisons between simulations and theoretical results are exposed, as well as a performance evaluation of our algorithms.

Mass spectrometry is nowadays the method of choice for protein characterization in proteomics. Computer algorithms and software have played an essential role in analyzing the large amount of mass spectrometry data produced in any proteomics experiment. The fundamental task of such analyses is to identify the peptide for each spectrum in the data. Such identification is called “database search” if it requires the assistance of a protein database, and called “de novo sequencing” if not. In the past 20 years, many database search software tools have been developed for peptide identification; and a particular one, Mascot, that was developed in 1999, became dominant in the market. While new tools were continuously published in the following decade, none has significantly improved Mascot. The situation was disrupted around 2010, when the field witnessed a flurry of new database search tools that significantly improved Mascot in terms of both accuracy and sensitivity. In the first part of the talk, the peptide identification problem will be introduced, and the history briefly reviewed. In the second part of the talk, some practical concerns for using the bioinformatics tools in a proteomics lab are discussed. Properly dealing with these concerns resulted into the significant improvement we witnessed in the past few years. The second part of the talk will be focused on the research conducted at the author’s own group. Z. Cai et al. (Eds.): ISBRA 2013, LNBI 7875, p. 1, 2013. c © Springer-Verlag Berlin Heidelberg 2013 Identifying Critical Transitions of Biological Processes by Dynamical Network Biomarkers

published or not.The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

The advent of the computer and of computer science, and in particular virtual reality, offers new experiment possibilities with numerical simulations and introduces a new type of investigation for the complex systems study : the in virtuo experiment. This work lies on the framework of object oriented-modeling and multiagent systems. The aim is to combine the systemic paradigm and the virtual reality. We propose a generic model for systems biology based on reification of the interactions, on a concept of organisation and on a multimodeling approach. By “reification” we understand that interactions are taken as autonomous agents. An application to a disease study : the allergic urticaria.

The use of a multiagent system (MAS) to model and simulate biochemical reactions is useful for several reasons. Four of them are, first, it is possible to represent directly each molecular type using a specific agent type, second, it is possible to duplicate the agent types in differents quantities to retrieve the real molecular concentrations, third, it is possible to make the agent reacting with each other under certain conditions or events in order to represent the reactions between the real molecules and fourth, the agents take place in a 2D or 3D environment where many situations and phenomena can be simulated. In this paper we detail different stages allowing the creation of a MAS representing one or more biochemical reactions occurring concurrently in a same environment.

Virtual Reality is becoming increasingly necessary to study complex systems such as biological systems. Thanks to Virtual Reality, the user is placed at the heart of biological simulations and can carry out experiments as if he were under the same experimental conditions as in vivo or in vitro. We usually call this kind of experiments in virtuo experiments.In order to rapidly develop Virtual Reality applications related to biology, we have already proposed the ReISCOP meta model which makes it possible to easily design biological simulations and undertake in virtuo experiments. This meta model allows to describe a biological system as a composition of its sub-systems and the interactions between the constituents of these sub-systems.Unfortunately, when using a single computer, the number of simulated entities is far from what is needed in biological simulations. It seemed thus necessary to extend the ReISCOP meta model so that it allows distributed computing on a grid. We made this choice because the structure of the ReISCOP meta model is well adapted to a distribution on a grid where the sub-systems which compose a system can be dispatched on different nodes, the synchronization and the coherence of the system being ensured by a Peer-to-Peer architecture. Unlike traditional approaches which propose a spatial distribution, the method we describe in this paper is based on an "organizational" distribution linked to the ReISCOP meta model. This "organizational" distribution is mainly ensured by using two efficient algorithms based on a dead reckoning method, one for a data consistency between nodes and one for a weak synchronization of the nodes involved. These two algorithms are integrated into the behaviors of agents DIVAs which are located on each node of the grid. These agents are able to communicate by using a Peer-to-Peer architecture upon the grid. In order to validate our approach, we implement three distributed simulations with increasing complexities and we compare the results with the results obtained in the non-distributed simulations. We get very similar results for the distributed and the non-distributed simulations.

We address the question of frequencies locking in coupled differential systems and of the existence of (component) quasi-periodic solutions of some kind of differential systems. These systems named cellular systems are quite general as they deal with countable number of coupled systems in some general Banach spaces. Moreover, the inner dynamics of each subsystem does not have to be specified. We reach some general results about how the frequencies locking phenomenon is related to the structure of the coupling map, and therefore about the localization of a certain type of quasi-periodic solutions of differential systems that may be seen as cellular systems. This paper gives some explanations about how and why synchronized behaviors naturally occur in a wide variety of complex systems.

One of the problems not clearly solved nowadays in food toxicology, is the evaluation of internal exposure to natural chemical contaminants. The worst solution is based on the concept that internal and external exposure are of the same level. One can go through this concept by considering biomarkers which can be used to calculate the inner contamination. Another solution is the use of a PBTK simulation method (Physiologically-Based Toxico-Kinetics). This method allows the knowledge of distribution of the contaminant over all the organs and their evolution in time. Unfortunately, the modeling and simulation processes are very hard to implement and use. The use of a technology issued from virtual modeling shows a good opportunity of developing a user interface easy to use for biologists. This interface is based on the multi interactions paradigm which is an evolution of the multi agent simulations.

The study of complex systems consists in considering entities submitted to interactions which define the dynamics of the system. Virtual reality opens the way to interactive simulation of complex systems, so called the in virtuo experimentation. For that purpose we use multi-interactions systems, based on the reification of interactions and multi-agent systems, in a phenomenological approach. Interaction agents represent the modeler understanding of the relations between the constituents of the system. Such descriptive models lead us to define parameters a priori. Moreover these parameters can be fluctuant, or even unknown, during a simulation in relation to the system dynamics or user interventions. To respond to this problem, we expose in this paper a redundant multiscale architecture which rests upon the fact that we can establish models of a same phenomenon at heterogeneous time and space scales. Heterogenous Multiscale Methods provide a general framework to mix levels of description of a system. Our intention is to implement this framework in multi-interactions systems by means of a Scale-Interaction agent. Then we illustrate our architecture through a pharmacokinetics application. Indeed biochemical kinetics abounds of parametric phenomena. Finally we discuss about some questions raised by this methodology, such as synchronicity, organization detection and genericity.