A rule-based expert system for chemical prioritization using effects-based chemical categories.

A rule-based expert system (ES) was developed to predict chemical binding to the estrogen receptor (ER) patterned on the research approaches championed by Gilman Veith to whom this article and journal issue are dedicated. The ERES was built to be mechanistically transparent and meet the needs of a specific application, i.e. predict for all chemicals within two well-defined inventories (industrial chemicals used as pesticide inerts and antimicrobial pesticides). These chemicals all lack structural features associated with high affinity binders and thus any binding should be low affinity. Similar to the high-quality fathead minnow database upon which Veith QSARs were built, the ERES was derived from what has been termed gold standard data, systematically collected in assays optimized to detect even low affinity binding and maximizing confidence in the negatives determinations. The resultant logic-based decision tree ERES, determined to be a robust model, contains seven major nodes with multiple effects-based chemicals categories within each. Predicted results are presented in the context of empirical data within local chemical structural groups facilitating informed decision-making. Even using optimized detection assays, the ERES applied to two inventories of >600 chemicals resulted in only ~5% of the chemicals predicted to bind ER.

Effects-based chemical category approach for prioritization of low affinity estrogenic chemicals.

Regulatory agencies are charged with addressing the endocrine disrupting potential of large numbers of chemicals for which there is often little or no data on which to make decisions. Prioritizing the chemicals of greatest concern for further screening for potential hazard to humans and wildlife is an initial step in the process. This paper presents the collection of in vitro data using assays optimized to detect low affinity estrogen receptor (ER) binding chemicals and the use of that data to build effects-based chemical categories following QSAR approaches and principles pioneered by Gilman Veith and colleagues for application to environmental regulatory challenges. Effects-based chemical categories were built using these QSAR principles focused on the types of chemicals in the specific regulatory domain of concern, i.e. non-steroidal industrial chemicals, and based upon a mechanistic hypothesis of how these non-steroidal chemicals of seemingly dissimilar structure to 17ß-estradiol (E2) could interact with the ER via two distinct binding types. Chemicals were also tested to solubility thereby minimizing false negatives and providing confidence in determination of chemicals as inactive. The high-quality data collected in this manner were used to build an ER expert system for chemical prioritization described in a companion article in this journal.

Experimentally verified mathematical approach for the prediction of force developed by motor units at variable frequency stimulation patterns.

During normal daily activity, muscle motor units (MUs) develop unfused tetanic contractions evoked by trains of motoneuronal firings at variable interpulse intervals (IPIs). The mechanical responses of a MU to successive impulses are not identical. The aim of this study was to develop a mathematical approach for the prediction of each response within the tetanus as well as the tetanic force itself. Experimental unfused tetani of fast and slow rat MUs, evoked by trains of stimuli at variable IPIs, were decomposed into series of twitch-shaped responses to successive stimuli using a previously described algorithm. The relationships between the parameters of the modeled twitches and the tetanic force level at which the next response begins were examined and regression equations were derived. Using these equations, profiles of force for the same and different stimulation patterns were mathematically predicted by summating modeled twitches. For comparison, force predictions were made by the summation of twitches equal to the first one. The recorded and the predicted tetanic forces were compared. The results revealed that it is possible to predict tetanic force with high accuracy by using regression equations. The force predicted in this way was much closer to the experimental record than the force obtained by the summation of equal twitches, especially for slow MUs. These findings are likely to have an impact on the development of realistic muscle models composed of MUs, and will assist our understanding of the significance of the neuronal code in motor control and the role of biophysical processes during MU contractions.

Strategic selection of chemicals for testing. Part I. Functionalities and performance of basic selection methods.

To develop quantitative structure-activity relationships (QSAR) models capable of predicting adverse effects for large chemical inventories and diverse structures, an interactive approach is presented that includes testing of strategically selected chemicals to expand the scope of a preliminary model to cover a target inventory. The goal of chemical selection in this context is to make the testing more effective in terms of adding maximal new structural information to the predictive model with minimal testing. The aim of this paper is to describe a set of algorithmic solutions and modelling techniques that can be used to efficiently select chemicals for testing to achieve a variety of goals. One purpose of chemical selection is to refine the model thus extending our knowledge about the modelled subject. Alternatively, the selection of chemicals for testing could be targeted at achieving a more adequate structural representation of a specific universe of untested chemicals to extend the model applicability domain on each subsequent step of model development. The chemical selection tools are collectively provided in a software package referred to as ChemPick. The system also allows the visualisation of chemical inventories and training sets in multidimensional (two and three dimensions) descriptor space. The software environment allows one or more datasets to be simultaneously loaded in a three-dimensional (or N-dimensional) chart where each point represents a combination of values for the descriptors for a given conformation of a chemical. The application of the chemical selection tools to select chemicals to expand a preliminary model of human oestrogen receptor (hER) ligand binding to more adequately cover a diverse chemical inventory is presented to demonstrate the application of these tools.

Variability of successive contractions subtracted from unfused tetanus of fast and slow motor units.

Stimulation of motor units (MUs) with repeated pulses evokes tetanic contractions, which consist of overlapping mechanical responses. The summation of these responses into tetanus is a nonlinear process due to the dynamic changes in the amplitudes and time parameters of the successive components. In order to study these changes, two MUs (one fast and one slow) of rat medial gastrocnemius muscle were stimulated with a progressively increasing number of pulses, from one (i=1) to sixteen (i=16) at a frequency of 15 Hz for the slow MU and 60 Hz for the fast MU. The individual responses were calculated by subtracting the (i)th from the (i+1)th tetanus recording. The contractions obtained following subtraction were modeled using a novel 6-parameter analytical function. The main conclusions of this study are (1) the newly presented analytical function is able to precisely describe the variable shape of all subtracted experimental contractions; (2) the shapes of successive contractions are variable and the subtracted contractions differ from the individual twitches; (3) as the pulse number increases, the parameters of the subtracted contractions change in a different manner for the slow and fast MUs: for the slow MU, the maximal forces and the time parameters increase considerably up to the 4th response, after which they remain nearly constant or show only a slight increase; for the fast MU, the maximal forces and durations also increase, whereas the remaining time parameters initially increase and then maintain a constant level or decrease, which explains the sag phenomenon visible in the unfused tetanus of fast MUs.

Modeling investigation of learning a fast elbow flexion in the horizontal plane--prediction of muscle forces and motor units action.

Experimental investigation of practicing a dynamic, goal-directed movement reveals significant changes in kinematics. Modeling can provide insight into the alterations in muscle activity, associated with the kinematic adaptations, and reveal the potential motor unit (MU) firing patterns that underlie those changes. In this paper, a previously developed muscle model and software (Raikova and Aladjov, Journal of Biomechanics, 35, 2002) have been used to investigate changes in MU control, while practicing fast elbow flexion to a target in the horizontal plane. The first trial (before practice) and the last trial (after extensive practice) of two subjects have been simulated. The inputs for the simulation were the calculated external moments at the elbow joint. The external moments were countered by the action of three flexor muscles and two extensor ones. The muscles have been modeled as a mixture of MUs of different types. The software has chosen the MU firing times necessary to accomplish the movement. The muscle forces and MUs firing statistics were then calculated. Three hypotheses were tested and confirmed: (1) peak muscle forces and antagonist co-contraction increase during training; (2) there is an increase in the firing frequency and the synchronization between MUs; and (3) the recruitment of fast-twitch MUs dominates the action.

Comparison between two muscle models under dynamic conditions.

One fundamental problem when trying to calculate the force developed by one muscle during a motor task is the muscle model. Usually, one control signal is juxtaposed to one musclotendon unit. The question is how is this signal connected to the activation of the motor units (MUs) that compose the muscle and fire differently. The aim of the paper is to compare a Hill-type muscle model to a model composed of MUs. A fast elbow flexion performed by only one muscle is considered. The activation necessary for performing the motion and the corresponding frequencies are calculated for cases of fast and slow muscles using Hill-type model. Then the muscle is modelled as a mixture of [774 MUs] with uniformly distributed twitch parameters. Using MotCo software the moments of impulsation of all MUs and their mechanical responses are predicted. The activation characteristics obtained by the two muscle models are compared. It is concluded that there are two essential parameters for proper muscle modelling: the lead-time and the MUs composition.