Modeling and Hemofiltration Treatment of Acute Inflammation.

The body responds to endotoxins by triggering the acute inflammatory response system to eliminate the threat posed by gram-negative bacteria (endotoxin) and restore health. However, an uncontrolled inflammatory response can lead to tissue damage, organ failure, and ultimately death; this is clinically known as sepsis. Mathematical models of acute inflammatory disease have the potential to guide treatment decisions in critically ill patients. In this work, an 8-state (8-D) differential equation model of the acute inflammatory response system to endotoxin challenge was developed. Endotoxin challenges at 3 and 12 mg/kg were administered to rats, and dynamic cytokine data for interleukin (IL)-6, tumor necrosis factor (TNF), and IL-10 were obtained and used to calibrate the model. Evaluation of competing model structures was performed by analyzing model predictions at 3, 6, and 12 mg/kg endotoxin challenges with respect to experimental data from rats. Subsequently, a model predictive control (MPC) algorithm was synthesized to control a hemoadsorption (HA) device, a blood purification treatment for acute inflammation. A particle filter (PF) algorithm was implemented to estimate the full state vector of the endotoxemic rat based on time series cytokine measurements. Treatment simulations show that: (i) the apparent primary mechanism of HA efficacy is white blood cell (WBC) capture, with cytokine capture a secondary benefit; and (ii) differential filtering of cytokines and WBC does not provide substantial improvement in treatment outcomes vs. existing HA devices.

Investigating inter-segmental connections between thoracic ganglia in the stick insect by means of experimental and simulated phase response curves.

The neuronal networks that control the motion of the individual legs in insects, in particular in the stick insect, are located in the pro-, meso- and meta-thoracic ganglia. They ensure high flexibility of movement control. Thus, the legs can move in an apparently independent way, e.g., during search movements, but also in tight coordination during locomotion. The latter is evidently a very important behavioural mode. It has, therefore, inspired a large number of studies, all aiming at uncovering the nature of the inter-leg coordination. One of the basic questions has been as to how the individual control networks in the three thoracic ganglia are connected to each other. One way to study this problem is to use phase response curves. They can reveal properties of the coupling between oscillatory systems, such as the central pattern generators in the control networks in the thoracic ganglia. In this paper, we report results that we have achieved by means of a combined experimental and modelling approach. We have calculated phase response curves from data obtained in as yet unpublished experiments as well as from those in previously published ones. By using models of the connected pro- and meso-thoracic control networks of the protractor and retractor neuromuscular systems, we have also produced simulated phase response curves and compared them with the experimental ones. In this way, we could gain important information of the nature of the connections between the aforementioned control networks. Specifically, we have found that connections from both the protractor and the retractor "sides" of the pro-thoracic network to the meso-thoracic one are necessary for producing phase response curves that show close similarity to the experimental ones. Furthermore, the strength of the excitatory connections has been proven to be crucial, while the inhibitory connections have essentially been irrelevant. We, thus, suggest that this type of connection might also be present in the stick insect, and possibly in other insect species.

A mathematical model of dysfunction of the thalamo-cortical loop in schizophrenia.

BACKGROUND: Recent experimental results suggest that impairment of auditory information processing in the thalamo-cortical loop is crucially related to schizophrenia. Large differences between schizophrenia patients and healthy controls were found in the cortical EEG signals. METHODS: We derive a phenomenological mathematical model, based on coupled phase oscillators with continuously distributed frequencies to describe the neural activity of the thalamo-cortical loop. We examine the influence of the bidirectional coupling strengths between the thalamic and the cortical area with regard to the phase-locking effects observed in the experiments. We extend this approach to a model consisting of a thalamic area coupled to two cortical areas, each comprising a set of nonidentical phase oscillators. In the investigations of our model, we applied the Ott-Antonsen theory and the Pikovsky-Rosenblum reduction methods to the original system. RESULTS: The results derived from our mathematical model satisfactorily reproduce the experimental data obtained by EEG measurements. Furthermore, they show that modifying the coupling strength from the thalamic region to a cortical region affects the duration of phase synchronization, while a change in the feedback to the thalamus affects the strength of synchronization in the cortex. In addition, our model provides an explanation in terms of nonlinear dynamics as to why brain waves desynchronize after a given phase reset. CONCLUSION: Our model can explain functional differences seen between EEG records of healthy subjects and schizophrenia patients on a system theoretic basis. Because of this and its predictive character, the model may be considered to pave the way towards an early and reliable clinical detection of schizophrenia that is dependent on the interconnections between the thalamic and cortical regions. In particular, the model parameter that describes the strength of this connection can be used for a diagnostic classification of schizophrenia patients.

A neuro-mechanical model of a single leg joint highlighting the basic physiological role of fast and slow muscle fibres of an insect muscle system.

In legged animals, the muscle system has a dual function: to produce forces and torques necessary to move the limbs in a systematic way, and to maintain the body in a static position. These two functions are performed by the contribution of specialized motor units, i.e. motoneurons driving sets of specialized muscle fibres. With reference to their overall contraction and metabolic properties they are called fast and slow muscle fibres and can be found ubiquitously in skeletal muscles. Both fibre types are active during stepping, but only the slow ones maintain the posture of the body. From these findings, the general hypothesis on a functional segregation between both fibre types and their neuronal control has arisen. Earlier muscle models did not fully take this aspect into account. They either focused on certain aspects of muscular function or were developed to describe specific behaviours only. By contrast, our neuro-mechanical model is more general as it allows functionally to differentiate between static and dynamic aspects of movement control. It does so by including both muscle fibre types and separate motoneuron drives. Our model helps to gain a deeper insight into how the nervous system might combine neuronal control of locomotion and posture. It predicts that (1) positioning the leg at a specific retraction angle in steady state is most likely due to the extent of recruitment of slow muscle fibres and not to the force developed in the individual fibres of the antagonistic muscles; (2) the fast muscle fibres of antagonistic muscles contract alternately during stepping, while co-contraction of the slow muscle fibres takes place during steady state; (3) there are several possible ways of transition between movement and steady state of the leg achieved by varying the time course of recruitment of the fibres in the participating muscles.

A neuro-mechanical model explaining the physiological role of fast and slow muscle fibres at stop and start of stepping of an insect leg.

Stop and start of stepping are two basic actions of the musculo-skeletal system of a leg. Although they are basic phenomena, they require the coordinated activities of the leg muscles. However, little is known of the details of how these activities are generated by the interactions between the local neuronal networks controlling the fast and slow muscle fibres at the individual leg joints. In the present work, we aim at uncovering some of those details using a suitable neuro-mechanical model. It is an extension of the model in the accompanying paper and now includes all three antagonistic muscle pairs of the main joints of an insect leg, together with their dedicated neuronal control, as well as common inhibitory motoneurons and the residual stiffness of the slow muscles. This model enabled us to study putative processes of intra-leg coordination during stop and start of stepping. We also made use of the effects of sensory signals encoding the position and velocity of the leg joints. Where experimental observations are available, the corresponding simulation results are in good agreement with them. Our model makes detailed predictions as to the coordination processes of the individual muscle systems both at stop and start of stepping. In particular, it reveals a possible role of the slow muscle fibres at stop in accelerating the convergence of the leg to its steady-state position. These findings lend our model physiological relevance and can therefore be used to elucidate details of the stop and start of stepping in insects, and perhaps in other animals, too.

Hill-type muscle model parameters determined from experiments on single muscles show large animal-to-animal variation.

Models built using mean data can represent only a very small percentage, or none, of the population being modeled, and produce different activity than any member of it. Overcoming this "averaging" pitfall requires measuring, in single individuals in single experiments, all of the system's defining characteristics. We have developed protocols that allow all the parameters in the curves used in typical Hill-type models (passive and active force-length, series elasticity, force-activation, force-velocity) to be determined from experiments on individual stick insect muscles (Blümel et al. 2012a). A requirement for means to not well represent the population is that the population shows large variation in its defining characteristics. We therefore used these protocols to measure extensor muscle defining parameters in multiple animals. Across-animal variability in these parameters can be very large, ranging from 1.3- to 17-fold. This large variation is consistent with earlier data in which extensor muscle responses to identical motor neuron driving showed large animal-to-animal variability (Hooper et al. 2006), and suggests accurate modeling of extensor muscles requires modeling individual-by-individual. These complete characterizations of individual muscles also allowed us to test for parameter correlations. Two parameter pairs significantly co-varied, suggesting that a simpler model could as well reproduce muscle response.

Quadrupedal gaits in hexapod animals - inter-leg coordination in free-walking adult stick insects.

The analysis of inter-leg coordination in insect walking is generally a study of six-legged locomotion. For decades, the stick insect Carausius morosus has been instrumental for unravelling the rules and mechanisms that control leg coordination in hexapeds. We analysed inter-leg coordination in C. morosus that freely walked on straight paths on plane surfaces with different slopes. Consecutive 1.7 s sections were assigned inter-leg coordination patterns (which we call gaits) based on footfall patterns. Regular gaits, i.e. wave, tetrapod or tripod gaits, occurred in different proportions depending on surface slopes. Tetrapod gaits were observed most frequently, wave gaits only occurred on 90 deg inclining slopes and tripod gaits occurred most often on 15 deg declining slopes, i.e. in 40% of the sections. Depending on the slope, 36-66% of the sections were assigned irregular gaits. Irregular gaits were mostly due to multiple stepping by the front legs, which is perhaps probing behaviour, not phase coupled to the middle legs' cycles. In irregular gaits, middle leg and hindleg coordination was regular, related to quadrupedal walk and wave gaits. Apparently, front legs uncouple from and couple to the walking system without compromising middle leg and hindleg coordination. In front leg amputees, the remaining legs were strictly coordinated. In hindleg and middle leg amputees, the front legs continued multiple stepping. The coordination of middle leg amputees was maladapted, with front legs and hindlegs performing multiple steps or ipsilateral legs being in simultaneous swing. Thus, afferent information from middle legs might be necessary for a regular hindleg stepping pattern.

A putative neuronal network controlling the activity of the leg motoneurons of the stick insect.

It is widely accepted that the electrical activity of motoneurons that drive locomotion in the stick insect are controlled by two separate mechanisms: (i) the frequency of the activity through the central pattern generator, which provides the rhythm of movement during locomotion and (ii) the 'magnitude' through circuits distinct from the earlier one. In this study, we show a possible way of how this control mechanism might be implemented in the nervous system of the stick insect by means of a network model. To do this, we had to define the 'magnitude' of the neuronal activity more precisely as the average number of spikes per unit time. The model was constructed on the basis of relevant electrophysiological and morphological data. However, only their integration in the model led to the novel properties that enable the network quickly to adapt the motoneuronal activity to central commands or sensory signals by changing both the firing pattern and intensity of the motoneuron discharges. The network would thus act as the controlling network for each of the muscle pairs that move the individual joints in each of the legs. Our model may contribute to a better understanding of the mechanisms that underlie the fast adaptive control of locomotion in this, and possibly in other types of locomotor systems.

From neuron to behavior: dynamic equation-based prediction of biological processes in motor control.

This article presents the use of continuous dynamic models in the form of differential equations to describe and predict temporal changes in biological processes and discusses several of its important advantages over discontinuous bistable ones, exemplified on the stick insect walking system. In this system, coordinated locomotion is produced by concerted joint dynamics and interactions on different dynamical scales, which is therefore difficult to understand. Modeling using differential equations possesses, in general, the potential for the inclusion of biological detail, the suitability for simulation, and most importantly, parameter manipulation to make predictions about the system behavior. We will show in this review article how, in case of the stick insect walking system, continuous dynamical system models can help to understand coordinated locomotion.

A mathematical modeling study of inter-segmental coordination during stick insect walking.

The biomechanical conditions for walking in the stick insect require a modeling approach that is based on the control of pairs of antagonistic motoneuron (MN) pools for each leg joint by independent central pattern generators (CPGs). Each CPG controls a pair of antagonistic MN pools. Furthermore, specific sensory feedback signals play an important role in the control of single leg movement and in the generation of inter-leg coordination or the interplay between both tasks. Currently, however, no mathematical model exists that provides a theoretical approach to understanding the generation of coordinated locomotion in such a multi-legged locomotor system. In the present study, I created such a theoretical model for the stick insect walking system, which describes the MN activity of a single forward stepping middle leg and helps to explain the neuronal mechanisms underlying coordinating information transfer between ipsilateral legs. In this model, CPGs that belong to the same leg, as well as those belonging to different legs, are connected by specific sensory feedback pathways that convey information about movements and forces generated during locomotion. The model emphasizes the importance of sensory feedback, which is used by the central nervous system to enhance weak excitatory and inhibitory synaptic connections from front to rear between the three thorax-coxa-joint CPGs. Thereby the sensory feedback activates caudal pattern generation networks and helps to coordinate leg movements by generating in-phase and out-of-phase thoracic MN activity.

An inter-segmental network model and its use in elucidating gait-switches in the stick insect.

Animal locomotion requires highly coordinated working of the segmental neuronal networks that control the limb movements. Experiments have shown that sensory signals originating from the extremities play a pivotal role in controlling locomotion patterns by acting on central networks. Based on the results from stick insect locomotion, we constructed an inter-segmental model comprising local networks for all three legs, i.e. for the pro-, meso- and meta-thorax, their inter-connections and the main sensory inputs modifying their activities. In the model, the local networks are uniform, and each of them consists of a central pattern generator (CPG) providing the rhythmic oscillation for the protractor-retractor motor systems, the corresponding motoneurons (MNs), and local inhibitory interneurons (IINs) between the CPGs and the MNs. Between segments, the CPGs are connected cyclically by both excitatory and inhibitory pathways that are modulated by the aforementioned sensory inputs. Simulations done with our network model showed that it was capable of reproducing basic patterns of locomotion such as those occurring during tri- and tetrapod gaits. The model further revealed a number of elementary neuronal processes (e.g. synaptic inhibition, or changing the synaptic drive at specific neurons) that in the simulations were necessary, and in their entirety sufficient, to bring about a transition from one type of gait to another. The main result of this simulation study is that exactly the same mechanism underlies the transition between the two types of gait irrespective of the direction of the change. Moreover, the model suggests that the majority of these processes can be attributed to direct sensory influences, and changes are required only in centrally controlled synaptic drives to the CPGs.

Dominance of local sensory signals over inter-segmental effects in a motor system: experiments.

Legged locomotion requires that information local to one leg, and inter-segmental signals coming from the other legs are processed appropriately to establish a coordinated walking pattern.However, very little is known about the relative importance of local and inter-segmental signals when they converge upon the central pattern generators (CPGs) of different leg joints.We investigated this question on the CPG of the middle leg coxa­trochanter (CTr)-joint of the stick insect which is responsible for lifting and lowering the leg.We used a semi-intact preparation with an intact front leg stepping on a treadmill, and simultaneously stimulated load sensors of the middle leg.We found that middle leg load signals induce bursts in the middle leg depressor motoneurons(MNs). The same local load signals could also elicit rhythmic activity in the CPG of the middle leg CTr-joint when the stimulation of middle leg load sensors coincided with front leg stepping. However, the influence of front leg stepping was generally weak such that front leg stepping alone was only rarely accompanied by switching between middle leg levator and depressor MN activity. We therefore conclude that the impact of the local sensory signals on the levator­depressor motor system is stronger than the inter-segmental influence through front leg stepping.

Dominance of local sensory signals over inter-segmental effects in a motor system: modeling studies.

Recent experiments, reported in the accompanying paper, have supplied key data on the impact afferent excitation has on the activity of the levator­depressor motor system of an extremity in the stick insect. The main finding was that, stimulation of the campaniform sensillae of the partially amputated middle leg in an animal where all other but one front leg had been removed, had a dominating effect over that of the stepping ipsilateral front leg. In fact,the latter effect was minute compared to the former. In this article, we propose a local network that involves the neuronal part of the levator­depressor motor system and use it to elucidate the mechanisms that underlie the generation of neuronal activity in the experiments. In particular, we show that by appropriately modulating the activity in the neurons of the central pattern generator of the levator­depressor motor system, we obtain activity patterns of the motoneurons in the model that closely resemble those found in extracellular recordings in the stick insect. In addition, our model predicts specific properties of these records which depend on the stimuli applied to the stick insect leg. We also discuss our results on the segmental mechanisms in the context of inter-segmental coordination.