John A. Halter

GenePath: a System for Automated Construction of Genetic Networks from Mutant Data

MOTIVATION Genetic networks are often used in the analysis of biological phenomena. In classical genetics, they are constructed manually from experimental data on mutants. The field lacks formalism to guide such analysis, and accounting for all the data becomes complicated when large amounts of data are considered. RESULTS We have developed GenePath, an intelligent assistant that automates the analysis of genetic data. GenePath employs expert-defined patterns to uncover gene relations from the data, and uses these relations as constraints in the search for a plausible genetic network. GenePath formalizes genetic data analysis, facilitates the consideration of all the available data in a consistent manner, and the examination of the large number of possible consequences of planned experiments. It also provides an explanation mechanism that traces every finding to the pertinent data. AVAILABILITY GenePath can be accessed at http://genepath.org. SUPPLEMENTARY INFORMATION Supplementary material is available at http://genepath.org/bi-.supp.

Development of a Clinical Monitoring System by Means of ICP Waveform Analysis

Recording the mean intracranial pressure (ICP) as an index of potential herniation has been the standard for intracranial monitoring. The mean intracranial pressure represents only one parameter of a complex control system. The mean ICP may not show variations until late in the course of a pathologic process or may never be elevated despite progressive injury to the brain. Intracranial compliance has been proposed to have a greater prognostic value than the mean ICP (Sullivan et al., 1980). The pressure volume index (PVI) is at present the most useful test for determination of intracranial compliance (Shapiro et al., 1980). The PVI is not suitable for continuous monitoring, since it requires volumetric manipulation of the cerebrospinal fluid (CSF). Similarly, currently used techniques for measurement of cerebral blood flow (CBF) are not suited to continuous monitoring (Langfitt, 1975).

GenePath: a system for inference of genetic networks and proposal of genetic experiments

A genetic network is a formalism that is often used in biology to represent causalities and reason about biological phenomena related to genetic regulation. We present GenePath, a computer-based system that supports the inference of genetic networks from a set of genetic experiments. Implemented in Prolog, GenePath uses abductive inference to elucidate network constraints based on background knowledge and experimental results. Additionally, it can propose genetic experiments that may further refine the discovered network and establish relations between genes that could not be related based on the original experimental data. We illustrate GenePaths approach and utility on analysis of data on aggregation and sporulation of the soil amoeba Dictyostelium discoideum.

Recovery of Amino Acid Neurotransmitters from the Spinal Cord During Posterior Epidural Stimulation: A Preliminary Study

Continuous posterior epidural spinal cord stimulation (SCS) has been an effective method for treating spasticity. The mechanisms of SCS include activation of inhibitory segmental neuronal systems and suprasegmental structures that produce inhibitory descending control. The neurochemical correlates of the mechanism of action have not been clearly defined. Microdialysis of the spinal cord extracellular space in an in vivo preparation during continuous epidural SCS was performed. The recovery of amino acid neurotransmitters, aspartate, glutamate, gamma-aminobutyric acid (GABA), glycine, and taurine from stimulated animals was compared to non-stimulated animals. Evoked potentials from the cortex and spinal cord of the stimulated animals were recorded to insure that there had been adequate epidural stimulation and normal segmental cord function. A significant increase in the concentration of glycine was seen after 90 minutes of continuous stimulation. The levels of the other amino acids were not significantly elevated. These results suggest that amelioration of spasticity with epidural SCS may involve enhanced glycine release, the major inhibitory neurotransmitter of the spinal cord.

Electrophrenic Respiration in Patients With High Quadriplegia

After determining that 15 patients with high spinal cord injuries who were permanently apneic had viable phrenic nerves, electrophrenic respiration units were implanted. Thirteen of the patients (86%) achieved full-time respiration and two more achieved half-time respiration. Despite the loss of 8 patients to unrelated problems, 7 now use electrophrenic respiration continuously, one having done so for 16 years. The patient selection criteria, neurophysiological evaluation method, surgical procedure, postoperative care, and methods for diagnosis of system failures are presented. A comparison of the cervical and thoracic procedures is made. The cervical approach is preferred. Complications consisted primarily of equipment failures. For the external components there were several cases of antenna connection and battery connection failures. The implanted receivers failed in 6 cases with an average lifetime of 48 months, ranging from 24 to 108 months. In one case fibrosis around the electrode resulted in failure to stimulate the phrenic nerve effectively. In another case, infection required removal of the system which was reimplanted later and has continued to provide successful ventilation.

Discovery of Genetic Networks Through Abduction and Qualitative Simulation

GenePath is an automated system for reasoning about genetic networks, wherein a set of genes have various influences on one another and on a biological outcome. It acts on a set of experiments in which genes are knocked-out or overexpressed, and the outcome of interest is evaluated. Implemented in Prolog, GenePath uses abductive inference to elucidate network constraints based on background knowledge and experimental results. Two uses of the system are demonstrated: synthesis of a consistent network from abduced constraints, and qualitative reasoning-based approach that generates a set of networks consistent with the data. In practice, as illustrated by an example on aggregation of a soil amoeba Dictyostelium discoideum, a combination of constraint satisfaction and qualitative reasoning produces a small set of plausible networks.

Web-Enabled Knowledge-Based Analysis of Genetic Data

We present a web-based implementation of GenePath, an intelligent assistant tool for data analysis in functional genomics. GenePath considers mutant data and uses expert-defined patterns to find gene-to-gene or gene-to-outcome relations. It presents the results of analysis as genetic networks, wherein a set of genes has various influence on one another and on a biological outcome. In the paper, we particularly focus on its web-based interface and explanation mechanisms.

Concept Discovery by Decision Table Decomposition and its Application in Neurophysiology

This chapter presents a “divide-and-conquer” data analysis method that, given a concept described by a decision table, develops its description in terms of intermediate concepts described by smaller and more manageable decision tables. The method is based on decision table decomposition, a machine learning approach that decomposes a given decision table into an equivalent hierarchy of decision tables. The decomposition aims to discover the decision tables that are overall less complex than the initial one, potentially easier to interpret, and introduce new and meaningful intermediate concepts. The chapter introduces the decomposition method and, through decomposition-based data analysis of two neurophysiological datasets, shows that the decomposition can discover physiologically meaningful concept hierarchies and construct interpretable decision tables which reveal relevant physiological principles.

An electrodiffusion model of the mammalian myelinated nerve fiber

A new model of the mammalian myelinated nerve fiber is presented which includes the representation of longitudinal electrodiffusion of component ions within the intra-axonal and periaxonal volumes. The model utilizes a non-uniform compartmental approximation to the detailed anatomy of the nodal and paranodal regions. The axonal membrane includes ionic pumps and multiple types of ionic channels whose spatial distribution and dynamics are derived from contemporary experimental studies. The model takes the form of a system of coupled non-linear parabolic partial differential equations with time-varying coefficients. A finite-difference approximation to this system is formed and solved utilizing an implicit numerical integration method. This model also includes a graphical user interface as well as a simulation management and optimization environment. The model reproduces conduction behavior seen in previous experimental and modeling efforts. Importantly, this model represents activity-dependent changes in ion concentration within the myelinated nerve fiber. In particular, significant changes can be seen in the concentration of potassium ions in the restricted periaxonal volume contained between the inner layer of the myelin sheath and the axon.

Study of conduction in a new model of the myelinated nerve fiber

A new mathematical model of the myelinated nerve fiber was applied to the study of conduction behavior. This model represents the myelinated nerve fiber as a multiaxial equivalent electric circuit incorporating separate intra-axonal, periaxonal and extra-axonal longitudinal conductive pathways with independent representations of the myelin sheath versus the underlying axolemmal membrane. A detailed anatomical representation of the node is included, with the periaxonal space extending to the nodal compartment. Both amphibian and mammalian nerve fibers were modeled. Model results show a physiological conduction velocity of 57.6 m/s for 17.5- mu m-diameter mammalian nerve fiber at 37 degrees C with a change in conduction velocity versus temperature closely correlated to experimental findings. The periaxonal space width and the axon radius in the paranodal region are shown to have a strong influence on conduction velocity.<<ETX>>