Mauno Rönkkö

Epigenetic Regulation of the Mammalian Cell

Background Understanding how mammalian cells are regulated epigenetically to express phenotype is a priority. The cellular phenotypic transition, induced by ionising radiation, from a normal cell to the genomic instability phenotype, where the ability to replicate the genotype accurately is compromised, illustrates important features of epigenetic regulation. Based on this phenomenon and earlier work we propose a model to describe the mammalian cell as a self assembled open system operating in an environment that includes its genotype, neighbouring cells and beyond. Phenotype is represented by high dimensional attractors, evolutionarily conditioned for stability and robustness and contingent on rules of engagement between gene products encoded in the genetic network. Methodology/Findings We describe how this system functions and note the indeterminacy and fluidity of its internal workings which place it in the logical reasoning framework of predicative logic. We find that the hypothesis is supported by evidence from cell and molecular biology. Conclusions Epigenetic regulation and memory are fundamentally physical, as opposed to chemical, processes and the transition to genomic instability is an important feature of mammalian cells with probable fundamental relevance to speciation and carcinogenesis. A source of evolutionarily selectable variation, in terms of the rules of engagement between gene products, is seen as more likely to have greater prominence than genetic variation in an evolutionary context. As this epigenetic variation is based on attractor states phenotypic changes are not gradual; a phenotypic transition can involve the changed contribution of several gene products in a single step.

An Artificial Ecosystem: Emergent Dynamics and Lifelike Properties

We discuss modeling and analysis of an artificial ecosystem. The ecosystem consists of basic elements, scents, plants, and animals. There are two species of animals: worms and beetles. As beetles absorb energy from worms, which absorb energy from blades of grass, which absorb energy from water, there is a food chain connecting animals to basic elements. The novelty of our approach lies in the modeling technique: we model the entire ecosystem using a single particle system. Consequently, the physical interaction dynamics not only shows emergent dynamics, but also some interesting lifelike properties. As the main contribution, we formalize the particle system and use it to model and analyze the ecosystem. We consider here several scenarios with nontrivial interaction dynamics.

Situational Knowledge Representation for Traffic Observed by a Pavement Vibration Sensor Network

Information systems that build on sensor networks often process data produced by measuring physical properties. These data can serve in the acquisition of knowledge for real-world situations that are of interest to information services and, ultimately, to people. Such systems face a common challenge, namely the considerable gap between the data produced by measurement and the abstract terminology used to describe real-world situations. We present and discuss the architecture of a software system that utilizes sensor data, digital signal processing, machine learning, and knowledge representation and reasoning to acquire, represent, and infer knowledge about real-world situations observable by a sensor network. We demonstrate the application of the system to vehicle detection and classification by measurement of road pavement vibration. Thus, real-world situations involve vehicles and information for their type, speed, and driving direction.

Representing situational knowledge acquired from sensor data for atmospheric phenomena

Abstract A recurrent problem in applications that build on environmental sensor networks is that of sensor data organization and interpretation. Organization focuses on, for instance, resolving the syntactic and semantic heterogeneity of sensor data. The distinguishing factor between organization and interpretation is the abstraction from sensor data with information acquired from sensor data. Such information may be situational knowledge for environmental phenomena. We discuss a generic software framework for the organization and interpretation of sensor data and demonstrate its application to data of a large scale sensor network for the monitoring of atmospheric phenomena. The results show that software support for the organization and interpretation of sensor data is valuable to scientists in scientific computing workflows. Explicitly represented situational knowledge is also useful to client software systems as it can be queried, integrated, reasoned, visualized, or annotated.

Making Sense of Sensor Data Using Ontology: A Discussion for Residential Building Monitoring

We illustrate the application of automated representation of knowledge acquired from sensor network data to quality of life services. Specifically, for a sensor network used to monitor a residential building we acquire knowledge about events of interest to occupants and represent such knowledge in ontology. An event of particular interest to quality of life which we discuss is ‘unhealthy’ exposure to carbon monoxide. Hence, we aim at reducing the considerable gap between raw sensor data and abstract domain terminology. Our results support the claim that computational techniques in signal processing, machine learning, and ontology engineering are important elements to systems that make use of environmental sensing, including systems for quality of life information services.

The Relevance of Measurement Data in Environmental Ontology Learning

Ontology has become increasingly important to software systems. The aim of ontology learning is to ease one of the major problems in ontology engineering, i.e. the cost of ontology construction. Much of the effort within the ontology learning community has focused on learning from text collections. However, environmental domains often deal with numerical measurement data and, therefore, rely on methods and tools for learning beyond text. We discuss this characteristic using two relations of an ontology for lakes. Specifically, we learn a threshold value from numerical measurement data for ontological rules that classify lakes according to nutrient status. We describe our methodology, highlight the cyclical interaction between data mining and ontologies, and note that the numerical value for lake nutrient status is specific to a spatial and temporal context. The use case suggests that learning from numerical measurement data is a research area relevant to environmental software systems.

Detection and Classification of Vehicles by Measurement of Road-Pavement Vibration and by Means of Supervised Machine Learning

Road vehicle detection and, to a lesser extent, classification have received considerable attention, in particular for the purpose of traffic monitoring by transportation authorities. A multitude of sensors and systems have been developed to assist people in traffic monitoring. Camera-based systems have enjoyed wide adoption over the last decade, partially substituting for more traditional techniques. Methods based on road-pavement vibration are not as common as camera-based systems. However, vibration sensors may be of interest when sensors must be out of sight and insensitive to environmental conditions, such as fog. We present and discuss our work on detection and classification of vehicles by measurement of road-pavement vibration and by means of supervised machine learning. We describe the entire processing chain from sensor data acquisition to vehicle classification and discuss our results for the task of vehicle detection and the task of vehicle classification separately. Using data for a single vibration sensor, our results show a performance ranging between 94% and near 100% for the detection task (1340 samples) and between 43% and 86% for the classification task (experiment specific, between 454 and 1243 samples).

Automated preprocessing of environmental data

In this article we discuss automated preprocessing of environmental data for further use. Environmental data is by default heterogeneous, as it may consist of data from sources such as weather stations, weather radars, chemical sensors, acoustic sensors, and off-line laboratory analysis. When integrating data from such heterogeneous sources, it needs to be processed in a context dependent manner. In addition, there is no single generic processing method; rather, several atomic methods need to be applied and in an appropriate sequence. Furthermore, the problem is complicated by the requirements set by the intended use of the data. The requirements influence not only the set of applicable methods but also the application sequence. In this article, we study automation of the selection and sequencing of preprocessing methods based on the user requirements. As the main contribution, we propose here the use of characterizations and a reachability algorithm to solve the selection and sequencing problem. In this article, we present the algorithm and argue for its correctness. We also discuss, how the algorithm is implemented as a cloud service, and illustrate the use of the service with simple case studies. A characterization based method for automated preprocessing of environmental data.A formalization of the preprocessing selection and sequencing problem.An algorithm solving the selection and sequencing problem.Simple case study implementation as a cloud service.

The evolutionary origin of form and function

We regard the basic unit of the organism, the cell, as a complex dissipative natural process functioning under the second law of thermodynamics and the principle of least action. Organisms are conglomerates of information bearing cells that optimise the efficiency of energy (nutrient) extraction from its ecosystem. Dissipative processes, such as peptide folding and protein interaction, yield phenotypic information from which form and function emerge from cell to cell interactions within the organism. Organisms, in Darwins ‘proportional numbers’, in turn interact to minimise the free energy of their ecosystems. Genetic variation plays no role in this holistic conceptualisation of the life process.

A Novel 3D-Based Network Simulation Platform for ZigBee Networks

In this paper, a novel 3D-based network simulation platform for ZigBee networks is proposed. The purpose of the platform is to improve the accuracy of radio propagation modelling and to offer designers a virtual workspace for testing their systems. Radio propagation models are investigated by performing path-loss calculations and Fresnel zone geometry estimation in a research laboratory environment.