Emanuela Merelli

BioWMS: a web-based Workflow Management System for bioinformatics

BackgroundAn in-silico experiment can be naturally specified as a workflow of activities implementing, in a standardized environment, the process of data and control analysis. A workflow has the advantage to be reproducible, traceable and compositional by reusing other workflows. In order to support the daily work of a bioscientist, several Workflow Management Systems (WMSs) have been proposed in bioinformatics. Generally, these systems centralize the workflow enactment and do not exploit standard process definition languages to describe, in order to be reusable, workflows. While almost all WMSs require heavy stand-alone applications to specify new workflows, only few of them provide a web-based process definition tool.ResultsWe have developed BioWMS, a Workflow Management System that supports, through a web-based interface, the definition, the execution and the results management of an in-silico experiment. BioWMS has been implemented over an agent-based middleware. It dynamically generates, from a user workflow specification, a domain-specific, agent-based workflow engine. Our approach exploits the proactiveness and mobility of the agent-based technology to embed, inside agents behaviour, the application domain features. Agents are workflow executors and the resulting workflow engine is a multiagent system – a distributed, concurrent system – typically open, flexible, and adaptative. A demo is available at http://litbio.unicam.it:8080/biowms.ConclusionBioWMS, supported by Hermes mobile computing middleware, guarantees the flexibility, scalability and fault tolerance required to a workflow enactment over distributed and heterogeneous environment. BioWMS is funded by the FIRB project LITBIO (Laboratory for Interdisciplinary Technologies in Bioinformatics).

An agent-oriented conceptual framework for systems biology

Recently, a collective effort from multiple research areas has been made to understand biological systems at the system level. On the one hand, for instance, researchers working on systems biology aim at understanding how living systems routinely perform complex tasks. On the other hand, bioscientists involved in pharmacogenomics strive to study how an individuals genetic inheritance affects the bodys response to drugs. Among the many things, research in the above disciplines requires the ability to simulate particular biological systems as cells, organs, organisms and communities. When observed according to the perspective of system simulation, biological systems are complex ones, and consist of a set of components interacting with each other and with an external (dynamic) environment. In this work, we propose an alternative way to specify and model complex systems based on behavioral modelling. We consider a biological system as a set of active computational components interacting in a dynamic and often unpredictable environment. Then, we propose a conceptual framework for engineering computational systems simulating the behaviour of biological systems, and modelling them in terms of agents and agent societies.

jHoles: A Tool for Understanding Biological Complex Networks via Clique Weight Rank Persistent Homology

Complex networks equipped with topological data analysis are one of the promising tools in the study of biological systems (e.g. evolution dynamics, brain correlation, breast cancer diagnosis, etc...). In this paper, we propose jHoles, a new version of Holes, an algorithms based on persistent homology for studying the connectivity features of complex networks. jHoles fills the lack of an efficient implementation of the filtering process for clique weight rank homology. We will give a brief overview of Holes, a more detailed description of jHoles algorithm, its implementation and the problem of clique weight rank homology. We present a biological case study showing how the connectivity of epidermal cells changes in response to a tumor presence. The biological network has been derived from the proliferative, differentiated and stratum corneum compartments, and jHoles used for studying variation of the connectivity.

BioShape: a spatial shape-based scale-independent simulation environment for biological systems

Abstract The simulation and visualization of biological system models is becoming more and more important both in clinical use and in basic research. Since many systems are characterized by interactions involving different scales at the same time, several approaches have been defined to handle such complex systems at different spatial and temporal scale. In this context, we propose BioShape, a 3D particle-based spatial simulator whose novelty consists of providing a uniform and geometry-oriented multiscale modeling environment. These features make BioShape “scaleindependent”, able to express geometric and positional information, and able to support transformations between scales simply defined as mappings between different granularity model instances. To highlight BioShape peculiarities, we sketch a multiscale model of human aortic valve where shapes are used at the cell scale for describing the interaction between a single valvular interstitial cell and its surrounding matrix, at the tissue scale for modeling the valve leaflet tissue mechanical behaviour, and at the organ scale for reproducing, as a 3D structure with fluid-structure interaction, the motion of the valve, blood, and surrounding tissue.

Transactions on Computational Systems Biology III

Computer-Aided DNA Base Calling from Forward and Reverse Electropherograms.- A Multi-agent System for Protein Secondary Structure Prediction.- Modeling Kohn Interaction Maps with Beta-Binders: An Example.- Multidisciplinary Investigation into Adult Stem Cell Behavior.- Statistical Model Selection Methods Applied to Biological Networks.- Using Secondary Structure Information to Perform Multiple Alignment.- Frequency Concepts and Pattern Detection for the Analysis of Motifs in Networks.- An Agent-Oriented Conceptual Framework for Systems Biology.- Genetic Linkage Analysis Algorithms and Their Implementation.- Abstract Machines of Systems Biology.

Topological Characterization of Complex Systems: Using Persistent Entropy

In this paper, we propose a methodology for deriving a model of a complex system by exploiting the information extracted from topological data analysis. Central to our approach is the S[B] paradigm in which a complex system is represented by a two-level model. One level, the structural S one, is derived using the newly-introduced quantitative concept of persistent entropy, and it is described by a persistent entropy automaton. The other level, the behavioral B one, is characterized by a network of interacting computational agents. The presented methodology is applied to a real case study, the idiotypic network of the mammalian immune system.

A multi-level model for self-adaptive systems

This work introduces a general multi-level model for self-adaptive systems. A self-adaptive system is seen as composed by two levels: the lower level describing the actual behaviour of the system and the upper level accounting for the dynamically changing environmental constraints on the system. In order to keep our description as general as possible, the lower level is modelled as a state machine and the upper level as a second-order state machine whose states have associated formulas over observable variables of the lower level. Thus, each state of the second-order machine identifies the set of lower-level states satisfying the constraints. Adaptation is triggered when a second-order transition is performed; this means that the current system no longer can satisfy the current high-level constraints and, thus, it has to adapt its behaviour by reaching a state that meets the new constraints. The semantics of the multi-level system is given by a flattened transition system that can be statically checked in order to prove the correctness of the adaptation model. To this aim we formalize two concepts of weak and strong adaptability providing both a relational and a logical characterization. We report that this work gives a formal computational characterization of multi-level self-adaptive systems, evidencing the important role that (theoretical) computer science could play in the emerging science of complex systems.

Interoperability issues among smart home technological frameworks

Population aging may be seen both as a human success story, the triumph of public health, medical advancements and economic development over diseases and injures, and as one of the most challenging phenomena that society faces in this century. Assistive technology in all its possible implementations (from Telemedicine to Ambient Assisted Living, and Ambient Intelligence) represents an emerging answer to the needs of the new generation of older adults whose desire is to live longer with a higher quality of life. Objective of this paper is to present the results of a public financed action for the development and implementation of an “integration platform” for Ambient Assisted Living that includes features of home automation (energy management, safety, comfort, etc.) and introduces “smart objects”, to monitor activities of daily living and detect any abnormal behavior that may represent a danger, or highlight symptoms of some incipient disease.

Multiple verification in complex biological systems: the bone remodelling case study

We present a set of formal techniques and a methodology for a composite formal analysis at the tissue and organ level, focusing on the verification of quantitative properties in the process of bone remodelling. Starting from a differential equation model, we derive a stochastic model and a piecewise multi-affine approximation in order to perform model checking of stabilisation properties for the biological tissue, and to assess the differences between a regular remodelling activity and a defective activity typical of pathologies like osteoporosis. The complex nonlinear dynamics of bone remodelling is analysed with a variety of techniques: sensitivity analysis for the differential equation model; quantitative probabilistic model checking for the stochastic model; and classical model checking and parameter synthesis on the piecewise multi-affine model. Such analyses allow us to extract a wealth of information that is not only useful for a deeper understanding of the biological process but also towards medical diagnoses.

A successive overrelaxation backpropagation algorithm for neural-network training

A variation of the classical backpropagation algorithm for neural network training is proposed and convergence is established using the perturbation results of Mangasarian and Solodov. The algorithm is similar to the successive overrelaxation (SOR) algorithm for systems of linear equations and linear complementary problems in using the most recently computed values of the weights to update the values on the remaining arcs.