Spatio-temporal reasoning for multi-scale modeling in cardiology

A general framework dedicated to computational morphogenesis Part II - Knowledge representation and architecture

In our previous paper we introduced morphogenesis and post-embryonic life as arising from cells interacting via coupled chemical, electrical and mechanical processes occurring across multiple organization levels. We reviewed these processes from the perspectives of developmental biology and how they relate to physics-based constitutive equations that are well suited to model intercellular interactions' fields. In this paper we will describe a knowledge representation and architectural design strategy that can organize and encode the biochemical, biological and biophysical data necessary to represent and model the highly specialized and diversified cells that constitute living tissues. Since there are about 200 different types of cells in mammalian tissues, a huge amount of molecular, cellular and tissue data must be accounted for. This data cannot be incorporated in an ad hoc manner but, on the contrary, must be organized according to some sound principles. We give an overview of these principles and describe how they can be incorporated as proper features of a Knowledge Base System (KBS) dedicated to computational morphogenesis (CM).

A general framework dedicated to computational morphogenesis Part I - Constitutive equations

In order to understand living organisms, considerable experimental efforts and resources have been devoted to correlate genes and their expressions with cell, tissue, organ and whole organisms' phenotypes. This data driven approach to knowledge discovery has led to many breakthrough in our understanding of healthy and diseased states, and is paving the way to improve the diagnosis and treatment of diseases. Complementary to this data-driven approach, computational models of biological systems based on first principles have been developed in order to deepen our understanding of the multi-scale dynamics that drives normal and pathological biological functions. In this paper we describe the biological, physical and mathematical concepts that led to the design of a Computational Morphogenesis (CM) platform baptized Generic Modeling and Simulating Platform (GMSP). Its role is to generate realistic 3D multi-scale biological tissues from virtual stem cells and the intended target applications include in virtuo studies of normal and abnormal tissue (re)generation as well as the development of complex diseases such as carcinogenesis. At all space-scales of interest, biological agents interact with each other via biochemical, bioelectrical, and mechanical fields that operate in concert during embryogenesis, growth and adult life. The spatio-temporal dependencies of these fields can be modeled by physics-based constitutive equations that we propose to examine in relation to the landmark biological events that occur during embryogenesis.

Temporal reasoning with medical data--a review with emphasis on medical natural language processing

Temporal information is crucial in electronic medical records and biomedical information systems. Processing temporal information in medical narrative data is a very challenging area. It lies at the intersection of temporal representation and reasoning (TRR) in artificial intelligence and medical natural language processing (MLP). Some fundamental concepts and important issues in relation to TRR have previously been discussed, mainly in the context of processing structured data in biomedical informatics; however, it is important that these concepts be re-examined in the context of processing narrative data using MLP. Theoretical and methodological TRR studies in biomedical informatics can be classified into three main categories: category 1 applies theories and models from temporal reasoning in AI; category 2 defines frameworks that meet needs from clinical applications; category 3 resolves issues such as temporal granularity and uncertainty. Currently, most MLP systems are not designed with a formal representation of time, and their ability to reason about temporal relations among medical events is limited. Previous work in processing time with clinical narrative data includes processing time in clinical reports, modeling textual temporal expressions in clinical databases, processing time in clinical guidelines, and building time standards for data exchange and integration. In addition to common problems in MLP, there are challenges specific to TRR in medical text, which occur at each level of linguistic structure and analysis. Despite advances in temporal reasoning in biomedical informatics, processing time in medical text deserves more attention. Besides the need for more research in temporal granularity, fuzzy time, temporal contradiction, intermittent events and uncertainty, broad areas for future research include enhancing functions of current MLP systems on processing temporal information, incorporating medical knowledge into temporal reasoning systems, resolving coreference, integrating narrative data with structured data and evaluating these systems.

A computer-aided approach to the structural analysis and modification of a large circulatory system model

The purpose of this study is to show an approach to making an intelligent support system for understanding and modifying a large circulatory system model using techniques of system analysis. Structural analysis makes it possible to visualize hierarchies of Coleman's circulatory model Human. Two techniques are successively applied for structural analysis, model reduction and graph analysis by interpretative structural modeling (ISM). First, the analysis for model reduction removes input-output relations with an input-output gain less than a given threshold, and second, the ISM technique applied to the reduced model of Human provides hierarchical directed graphs. The proposed approach: 1) enables visualization of a hierarchy graph of cause and effect relations of the large circulatory model, 2) suggests control and diagnostic information to the model by tracing back a path in the hierarchy, and 3) allows the user to modify the circulatory model. The efficiency and performance of the proposed approach demonstrates technical indications of success in analyzing and justifying experimental evidences with the online help of the system.

An interactive 3D anisotropic cellular automata model of the heart

A 3D cellular anisotropic automata model with modifiable geometry is described. The modeling parameters include grain size, fiber orientation, and free-wall and septal thickness. From this modifiable model, three specific models corresponding to normal heart, left ventricular hypertrophy, and ventricular dilatation were generated. Each model is a conduction and propagation model in which the atria, the major atrial vessel bases, the ventricles, and the specialized conduction system are represented. Muscle tissues are modeled as bundles of fibers with anisotropic conduction speed of the activation wavefronts. Regional variations of conduction, refractory gradients, and regional potential gradients can also be specified before each simulation. Each element has adaptive properties with respect to cycle length and to the prematurity of incoming impulses. Action potentials can be specified for each cell and an equivalent source formulation is carried out to simulate the vectorcardiogram and the corresponding 12-standard-lead electrocardiogram.