Adrien Elena

Robustness in Regulatory Networks: A Multi-Disciplinary Approach

We give in this paper indications about the dynamical impact (as phenotypic changes) coming from the main sources of perturbation in biological regulatory networks. First, we define the boundary of the interaction graph expressing the regulations between the main elements of the network (genes, proteins, metabolites, ...). Then, we search what changes in the state values on the boundary could cause some changes of states in the core of the system (robustness to boundary conditions). After, we analyse the role of the mode of updating (sequential, block sequential or parallel) on the asymptotics of the network, essentially on the occurrence of limit cycles (robustness to updating methods). Finally, we show the influence of some topological changes (e.g. suppression or addition of interactions) on the dynamical behaviour of the system (robustness to topology perturbations).

Robustness in Regulatory Interaction Networks. A Generic Approach with Applications at Different Levels: Physiologic, Metabolic and Genetic

Regulatory interaction networks are often studied on their dynamical side (existence of attractors, study of their stability). We focus here also on their robustness, that is their ability to offer the same spatiotemporal patterns and to resist to external perturbations such as losses of nodes or edges in the networks interactions architecture, changes in their environmental boundary conditions as well as changes in the update schedule (or updating mode) of the states of their elements (e.g., if these elements are genes, their synchronous coexpression mode versus their sequential expression). We define the generic notions of boundary, core, and critical vertex or edge of the underlying interaction graph of the regulatory network, whose disappearance causes dramatic changes in the number and nature of attractors (e.g., passage from a bistable behaviour to a unique periodic regime) or in the range of their basins of stability. The dynamic transition of states will be presented in the framework of threshold Boolean automata rules. A panorama of applications at different levels will be given: brain and plant morphogenesis, bulbar cardio-respiratory regulation, glycolytic/oxidative metabolic coupling, and eventually cell cycle and feather morphogenesis genetic control.

Micro-RNAs: viral genome and robustness of gene expression in the host

For comparing RNA rings or hairpins with reference or random ring sequences, circular versions of distances and distributions like those of Hamming and Gumbel are needed. We define these circular versions and we apply these new tools to the comparison of RNA relics (such as micro-RNAs and tRNAs) with viral genomes that have coevolved with them. Then we show how robust are the regulation networks incorporating in their boundary micro-RNAs as sources or new feedback loops involving ubiquitous proteins like p53 (which is a micro-RNA transcription factor) or oligopeptides regulating protein translation. Eventually, we propose a new coevolution game between viral and host genomes.

Determination, Optimization and Taxonomy of Regulatory Networks: The Example of Arabidopsis thaliana Flower Morphogenesis

This paper aims at warning modellers in systems biology against several traps encountered in the modelling of Boolean thresholded automata networks, i.e. the Hopfield-like networks that are often used in the context of neural and genetic networks. It introduces a new manner based on inverse methods to conceive such models. Using these techniques, we re-visit the model of regulatory network of Arabidopsis thaliana morphogenetic network. In this context, we discuss about the non-uniqueness of models, on a possible taxonomy of the set of valid models and on the sense of the relative size of the basin of attractions within or between these models.

Regulatory Networks Analysis: Robustness in Morphogenesis Regulation

The biological regulatory networks present common features because they have evolved until obtaining robust architecture of interactions, able to resist to perturbations like changes in boundary conditions like those induced by microRNAs or in updating rules deciding what genes are co-expressed. We present here some examples of these types of robustness by successively studying the boundary and updating influence on the core genes expression in morphogenesis regulatory networks controlling i) cell cycle in mammals, ii) feathers morphogenesis & iii) gastrulation in chicken, and iv) dorso-ventral stripes patterning in Drosophila.

Serious Games and Personalization of the Therapeutic Education

Therapeutic education uses currently serious games techniques, to have more impact on persons with a chronic disease at their place of living. This requires to customize the game so that the person attempts to change in his lifestyle and dietary habits, according to the specific advices given at relevant moments by the therapeutic monitoring. Thus, taking better account of alimentary and sedentary own habits, and those of his family or professionnel environment, it is possible to build a personalized educational tool, which evolves according the transformations of the pathotology (toward stabilization or to complication), taking into account an actimetry objectified by sensors, complementing and overlapping the information obtained by subjective reporting declarative procedures of dialogue with the virtual coach, during the game. We take as an example the sequence obesity/type 2 diabetis, which affects between 4% and 10% of older people in most developed and developing countries

Smart Homes and Sensors for Surveillance and Preventive Education at Home: Example of Obesity

(1) Background: The aim of this paper is to show that e-health tools like smart homes allow the personalization of the surveillance and preventive education of chronic patients, such as obese persons, in order to maintain a comfortable and preventive lifestyle at home. (2) Technologies and methods: Several types of sensors allow coaching the patient at home, e.g., the sensors recording the activity and monitoring the physiology of the person. All of this information serves to personalize serious games dedicated to preventive education, for example in nutrition and vision. (3) Results: We built a system of personalized preventive education at home based on serious games, derived from the feedback information they provide through a monitoring system. Therefore, it is possible to define (after clustering and personalized calibration) from the at home surveillance of chronic patients different comfort zones where their behavior can be estimated as normal or abnormal and, then, to adapt both alarm levels for surveillance and education programs for prevention, the chosen example of application being obesity.

Formal Methods for Hopfield-Like Networks

Building a meaningful model of biological regulatory network is usually done by specifying the components (e.g. the genes) and their interactions, by guessing the values of parameters, by comparing the predicted behaviors to the observed ones, and by modifying in a trial-error process both architecture and parameters in order to reach an optimal fitness. We propose here a different approach to construct and analyze biological models avoiding the trial-error part, where structure and dynamics are represented as formal constraints. We apply the method to Hopfield-like networks, a formalism often used in both neural and regulatory networks modeling. The aim is to characterize automatically the set of all models consistent with all the available knowledge (about structure and behavior). The available knowledge is formalized into formal constraints. The latter are compiled into Boolean formula in conjunctive normal form and then submitted to a Boolean satisfiability solver. This approach allows to formulate a wide range of queries, expressed in a high level language, and possibly integrating formalized intuitions. In order to explore its potential, we use it to find cycles for 3-nodes networks and to determine the flower morphogenesis regulatory network of Arabidopsis thaliana. Applications of this technique are numerous and concern the building of models from data as well as the design of biological networks possessing specified behaviors.