1,100 research outputs found
Agents in Bioinformatics
The scope of the Technical Forum Group (TFG) on Agents in Bioinformatics (BIOAGENTS) was to inspire collaboration between the agent and bioinformatics communities with the aim of creating an opportunity to propose a different (agent-based) approach to the development of computational frameworks both for data analysis in bioinformatics and for system modelling in computational biology. During the day, the participants examined the future of research on agents in bioinformatics primarily through 12 invited talks selected to cover the most relevant topics. From the discussions, it became clear that there are many perspectives to the field, ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages for use by information agents, and to the use of Grid agents, each of which requires further exploration. The interactions between participants encouraged the development of applications that describe a way of creating agent-based simulation models of biological systems, starting from an hypothesis and inferring new knowledge (or relations) by mining and analysing the huge amount of public biological data. In this report we summarise and reflect on the presentations and discussions
Adaptability Checking in Multi-Level Complex Systems
A hierarchical model for multi-level adaptive systems is built on two basic
levels: a lower behavioural level B accounting for the actual behaviour of the
system and an upper structural level S describing the adaptation dynamics of
the system. The behavioural level is modelled as a state machine and the
structural level as a higher-order system whose states have associated logical
formulas (constraints) over observables of the behavioural level. S is used to
capture the global and stable features of B, by a defining set of allowed
behaviours. The adaptation semantics is such that the upper S level imposes
constraints on the lower B level, which has to adapt whenever it no longer can
satisfy them. In this context, we introduce weak and strong adaptabil- ity,
i.e. the ability of a system to adapt for some evolution paths or for all
possible evolutions, respectively. We provide a relational characterisation for
these two notions and we show that adaptability checking, i.e. deciding if a
system is weak or strong adaptable, can be reduced to a CTL model checking
problem. We apply the model and the theoretical results to the case study of
motion control of autonomous transport vehicles.Comment: 57 page, 10 figures, research papaer, submitte
A Graph Grammar for Modelling RNA Folding
We propose a new approach for modelling the process of RNA folding as a graph
transformation guided by the global value of free energy. Since the folding
process evolves towards a configuration in which the free energy is minimal,
the global behaviour resembles the one of a self-adaptive system. Each RNA
configuration is a graph and the evolution of configurations is constrained by
precise rules that can be described by a graph grammar.Comment: In Proceedings GaM 2016, arXiv:1612.0105
Modelling and analysis of biochemical signalling pathway cross-talk
Signalling pathways are abstractions that help life scientists structure the coordination of cellular activity. Cross-talk between pathways accounts for many of the complex behaviours exhibited by signalling pathways and is often critical in producing the correct signal-response relationship. Formal models of signalling pathways and cross-talk in particular can aid understanding and drive experimentation. We define an approach to modelling based on the concept that a pathway is the (synchronising) parallel composition of instances of generic modules (with internal and external labels). Pathways are then composed by (synchronising) parallel composition and renaming; different types of cross-talk result from different combinations of synchronisation and renaming. We define a number of generic modules in PRISM and five types of cross-talk: signal flow, substrate availability, receptor function, gene expression and intracellular communication. We show that Continuous Stochastic Logic properties can both detect and distinguish the types of cross-talk. The approach is illustrated with small examples and an analysis of the cross-talk between the TGF-b/BMP, WNT and MAPK pathways
Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks
Gene and protein networks are very important to model complex large-scale
systems in molecular biology. Inferring or reverseengineering such networks can
be defined as the process of identifying gene/protein interactions from
experimental data through computational analysis. However, this task is
typically complicated by the enormously large scale of the unknowns in a rather
small sample size. Furthermore, when the goal is to study causal relationships
within the network, tools capable of overcoming the limitations of correlation
networks are required. In this work, we make use of Bayesian Graphical Models
to attach this problem and, specifically, we perform a comparative study of
different state-of-the-art heuristics, analyzing their performance in inferring
the structure of the Bayesian Network from breast cancer data
Non locality, Topology, Formal Languages: New Global Tools to Handle Large Data Sets
AbstractThe basic idea that stems out of this work is that large sets of data can be handled through an organized set of mathematical and computational tools rooted in a global geometric vision of data space allowing to explore the structure and hidden information patterns thereof. Based on this perspective, the objective is naturally that of discovering and letting emerge, directly from probing the data space, the manifold hidden relations (patterns), e.g. correlations among facts, interactions among entities, relations among concepts and formally describing, in a semantic mining context, the discovered information. In this note, we propose an approach that exploits topological methods for classifying global information into equivalence classes and regular languages for describing the corresponding automaton as element an of hidden complex system
The Topological Field Theory of Data: a program towards a novel strategy for data mining through data language
This paper aims to challenge the current thinking in IT for the 'Big Data' question, proposing - almost verbatim, with no formulas - a program aiming to construct an innovative methodology to perform data analytics in a way that returns an automaton as a recognizer of the data language: a Field Theory of Data. We suggest to build, directly out of probing data space, a theoretical framework enabling us to extract the manifold hidden relations (patterns) that exist among data, as correlations depending on the semantics generated by the mining context. The program, that is grounded in the recent innovative ways of integrating data into a topological setting, proposes the realization of a Topological Field Theory of Data, transferring and generalizing to the space of data notions inspired by physical (topological) field theories and harnesses the theory of formal languages to define the potential semantics necessary to understand the emerging patterns
An Individual-based Probabilistic Model for Fish Stock Simulation
We define an individual-based probabilistic model of a sole (Solea solea)
behaviour. The individual model is given in terms of an Extended Probabilistic
Discrete Timed Automaton (EPDTA), a new formalism that is introduced in the
paper and that is shown to be interpretable as a Markov decision process. A
given EPDTA model can be probabilistically model-checked by giving a suitable
translation into syntax accepted by existing model-checkers. In order to
simulate the dynamics of a given population of soles in different environmental
scenarios, an agent-based simulation environment is defined in which each agent
implements the behaviour of the given EPDTA model. By varying the probabilities
and the characteristic functions embedded in the EPDTA model it is possible to
represent different scenarios and to tune the model itself by comparing the
results of the simulations with real data about the sole stock in the North
Adriatic sea, available from the recent project SoleMon. The simulator is
presented and made available for its adaptation to other species.Comment: In Proceedings AMCA-POP 2010, arXiv:1008.314
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