Dynamical systems theory in physiology

Relation Between Obesity and Type 2 Diabetes: Evolutionary Insights, Perspectives and Controversies

PURPOSE OF REVIEW

Since the mid-twentieth century, obesity and its related comorbidities, notably insulin resistance (IR) and type 2 diabetes (T2D), have surged. Nevertheless, their underlying mechanisms remain elusive. Evolutionary medicine (EM) sheds light on these issues by examining how evolutionary processes shape traits and diseases, offering insights for medical practice. This review summarizes the pathogenesis and genetics of obesity-related IR and T2D. Subsequently, delving into their evolutionary connections. Addressing limitations and proposing future research directions aims to enhance our understanding of these conditions, paving the way for improved treatments and prevention strategies.

RECENT FINDINGS

Several evolutionary hypotheses have been proposed to unmask the origin of obesity-related IR and T2D, e.g., the "thrifty genotype" hypothesis suggests that certain "thrifty genes" that helped hunter-gatherer populations efficiently store energy as fat during feast-famine cycles are now maladaptive in our modern obesogenic environment. The "drifty genotype" theory suggests that if thrifty genes were advantageous, they would have spread widely, but proposes genetic drift instead. The "behavioral switch" and "carnivore connection" hypotheses propose insulin resistance as an adaptation for a brain-dependent, low-carbohydrate lifestyle. The thrifty phenotype theory suggests various metabolic outcomes shaped by genes and environment during development. However, the majority of these hypotheses lack experimental validation. Understanding why ancestral advantages now predispose us to diseases may aid in drug development and prevention of disease. EM helps us to understand the evolutionary relation between obesity-related IR and T2D. But still gaps and contradictions persist. Further interdisciplinary research is required to elucidate complete mechanisms.

Reconstructing computational system dynamics from neural data with recurrent neural networks

Computational models in neuroscience usually take the form of systems of differential equations. The behaviour of such systems is the subject of dynamical systems theory. Dynamical systems theory provides a powerful mathematical toolbox for analysing neurobiological processes and has been a mainstay of computational neuroscience for decades. Recently, recurrent neural networks (RNNs) have become a popular machine learning tool for studying the non-linear dynamics of neural and behavioural processes by emulating an underlying system of differential equations. RNNs have been routinely trained on similar behavioural tasks to those used for animal subjects to generate hypotheses about the underlying computational mechanisms. By contrast, RNNs can also be trained on the measured physiological and behavioural data, thereby directly inheriting their temporal and geometrical properties. In this way they become a formal surrogate for the experimentally probed system that can be further analysed, perturbed and simulated. This powerful approach is called dynamical system reconstruction. In this Perspective, we focus on recent trends in artificial intelligence and machine learning in this exciting and rapidly expanding field, which may be less well known in neuroscience. We discuss formal prerequisites, different model architectures and training approaches for RNN-based dynamical system reconstructions, ways to evaluate and validate model performance, how to interpret trained models in a neuroscience context, and current challenges.

Machine learning provides insight into models of heterogeneous electrical activity in human beta-cells

Understanding how heterogeneous cellular responses emerge from cell-to-cell variations in expression and function of subcellular components is of general interest. Here, we focus on human insulin-secreting beta-cells, which are believed to constitute a population in which heterogeneity is of physiological importance. We exploit recent single-cell electrophysiological data that allow biologically realistic population modeling of human beta-cells that accounts for cellular heterogeneity and correlation between ion channel parameters. To investigate how ion channels influence the dynamics of our updated mathematical model of human pancreatic beta-cells, we explore several machine learning techniques to determine which model parameters are important for determining the qualitative patterns of electrical activity of the model cells. As expected, K^＋ channels promote absence of activity, but once a cell is active, they increase the likelihood of having action potential firing. HERG channels were of great importance for determining cell behavior in most of the investigated scenarios. Fast bursting is influenced by the time scales of ion channel activation and, interestingly, by the type of Ca^2＋ channels coupled to BK channels in BK-CaV complexes. Slow, metabolically driven oscillations are promoted mostly by K(ATP) channels. In summary, combining population modeling with machine learning analysis provides insight into the model and generates new hypotheses to be investigated both experimentally, via simulations and through mathematical analysis.

Causal mechanisms of a scapular stabilization intervention for patients with subacromial pain syndrome: a secondary analysis of a randomized controlled trial

Background

Causal mediation analysis is one way to bridge this gap by exploring the causal pathways of a given intervention. The aim of this study was to assess whether scapular motion, position, and periscapular muscle strength are mediators for pain and shoulder disability outcomes following a scapular stabilization intervention for patients with subacromial pain syndrome.

Methods

Sixty patients were randomized into two groups: scapular stabilization or periscapular strengthening exercises. The intervention consisted of three sessions per week for 8 weeks. The primary outcome measures were pain and disability and the following outcome measures were considered as potential mediators: scapular motion, scapular position, periscapular muscle strength, age, duration of symptoms, and side of the complaint. A model-based inference approach with bootstrap simulations was used to estimate the average causal mediation effect, average direct effect, and the average total effect from the data of a randomized clinical trial that evaluated the effect of adding scapular stabilization exercises to a scapulothoracic strengthening program in people with subacromial pain syndrome.

Results

The results demonstrated that none of the putative mediators were influenced by the intervention. However, muscle strength of serratus anterior, upper, middle, and lower trapezius muscles was associated with shoulder disability.

Conclusion

Scapular kinematic and periscapular muscle strength did not mediate the effect of scapular stabilization exercises on shoulder pain or disability scores in subjects with subacromial pain syndrome. Muscle strength of serratus anterior, upper, middle and lower trapezius were associated with shoulder disability scores at 8-weeks follow-up.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40945-022-00138-1.

Quantitative roles of ion channel dynamics on ventricular action potential

Mathematical models for the action potential (AP) generation of the electrically excitable cells including the heart are involved different mechanisms including the voltage-dependent currents with nonlinear time- and voltage-gating properties. From the shape of the AP waveforms to the duration of the refractory periods or heart rhythms are greatly affected by the functions describing the features or the quantities of these ion channels. In this work, a mathematical measure to analyze the regional contributions of voltage-gated channels is defined by dividing the AP into phases, epochs, and intervals of interest. The contribution of each time-dependent current for the newly defined cardiomyocyte model is successfully calculated and it is found that the contribution of dominant ion channels changes substantially not only for each phase but also for different regions of the cardiac AP. Besides, the defined method can also be applied in all Hodgkin-Huxley types of electrically excitable cell models to be able to understand the underlying dynamics better.

Fast-slow analysis as a technique for understanding the neuronal response to current ramps

The standard protocol for studying the spiking properties of single neurons is the application of current steps while monitoring the voltage response. Although this is informative, the jump in applied current is artificial. A more physiological input is where the applied current is ramped up, reflecting chemosensory input. Unsurprisingly, neurons can respond differently to the two protocols, since ion channel activation and inactivation are affected differently. Understanding the effects of current ramps, and changes in their slopes, is facilitated by mathematical models. However, techniques for analyzing current ramps are under-developed. In this article, we demonstrate how current ramps can be analyzed in single neuron models. The primary issue is the presence of gating variables that activate on slow time scales and are therefore far from equilibrium throughout the ramp. The use of an appropriate fast-slow analysis technique allows one to fully understand the neural response to ramps of different slopes. This study is motivated by data from olfactory bulb dopamine neurons, where both fast ramp (tens of milliseconds) and slow ramp (tens of seconds) protocols are used to understand the spiking profiles of the cells. The slow ramps generate experimental bifurcation diagrams with the applied current as a bifurcation parameter, thereby establishing asymptotic spiking activity patterns. The faster ramps elicit purely transient behavior that is of relevance to most physiological inputs, which are short in duration. The two protocols together provide a broader understanding of the neuron's spiking profile and the role that slowly activating ion channels can play.

Using optimal control to understand complex metabolic pathways

BACKGROUND

Optimality principles have been used to explain the structure and behavior of living matter at different levels of organization, from basic phenomena at the molecular level, up to complex dynamics in whole populations. Most of these studies have assumed a single-criteria approach. Such optimality principles have been justified from an evolutionary perspective. In the context of the cell, previous studies have shown how dynamics of gene expression in small metabolic models can be explained assuming that cells have developed optimal adaptation strategies. Most of these works have considered rather simplified representations, such as small linear pathways, or reduced networks with a single branching point, and a single objective for the optimality criteria.

RESULTS

Here we consider the extension of this approach to more realistic scenarios, i.e. biochemical pathways of arbitrary size and structure. We first show that exploiting optimality principles for these networks poses great challenges due to the complexity of the associated optimal control problems. Second, in order to surmount such challenges, we present a computational framework which has been designed with scalability and efficiency in mind, including mechanisms to avoid the most common pitfalls. Third, we illustrate its performance with several case studies considering the central carbon metabolism of S. cerevisiae and B. subtilis. In particular, we consider metabolic dynamics during nutrient shift experiments.

CONCLUSIONS

We show how multi-objective optimal control can be used to predict temporal profiles of enzyme activation and metabolite concentrations in complex metabolic pathways. Further, we also show how to consider general cost/benefit trade-offs. In this study we have considered metabolic pathways, but this computational framework can also be applied to analyze the dynamics of other complex pathways, such as signal transduction or gene regulatory networks.

Measurement of scapular prominence in symptomatic dyskinesis using a novel scapulometer: reliability and the relationship to shoulder dysfunction

BACKGROUND

No previous studies have investigated whether the extent of scapular dyskinesis is associated with shoulder dysfunction. This study aimed (1) to establish the reliability of a scapulometer in patients with shoulder pain and (2) to investigate the related factors associated with shoulder dysfunction.

METHODS

One hundred participants with symptomatic scapular dyskinesis were recruited. Twenty-one participants were involved in the reliability study to test the intrarater and inter-rater reliabilities of the scapulometer in patients with shoulder pain. After demographic data and self-reported Flexilevel Scale of Shoulder Function (FLEX-SF) scores were recorded, all participants were measured with a scapulometer to determine the posterior displacement of the root of the spine (ROS) and the inferior angle (IFA) of the scapula from the thorax. Next, the participants performed 5 trials of bilateral scapular plane elevation for scapular kinematics and electromyographic (EMG) data collection. Stepwise multiple linear regressions were used to determine the relationships between self-reported FLEX-SF scores and potential factors. In addition to scapular displacement, pain level, scapular kinematics, and EMG data were included as independent variables.

RESULTS

The intrarater and inter-rater reliabilities of the scapulometer were excellent (intraclass correlation coefficient [ICC] = 0.93-0.97) and moderate to good (ICC = 0.74-0.81), respectively. The Bland-Altman plots showed no systematic bias between raters in the ROS and IFA measurements. Final stepwise multiple regression models showed that more ROS distance, higher serratus anterior activity, and lower pain level during arm elevation were associated with higher shoulder function (total R² = 0.253).

CONCLUSION

The reliability of the scapulometer in patients with shoulder pain is moderate to excellent. Scapular dyskinesis may be a compensatory strategy to avoid shoulder pain and improve shoulder function.

Systems Medicine Disease: Disease Classification and Scalability Beyond Networks and Boundary Conditions

In order to accommodate the forthcoming wealth of health and disease related information, from genome to body sensors to population and the environment, the approach to disease description and definition demands re-examination. Traditional classification methods remain trapped by history; to provide the descriptive features that are required for a comprehensive description of disease, systems science, which realizes dynamic processes, adaptive response, and asynchronous communication channels, must be applied (Wolkenhauer et al., 2013). When Disease is viewed beyond the thresholds of lines and threshold boundaries, disease definition is not only the result of reductionist, mechanistic categories which reluctantly face re-composition. Disease is process and synergy as the characteristics of Systems Biology and Systems Medicine are included. To capture the wealth of information and contribute meaningfully to medical practice and biology research, Disease classification goes beyond a single spatial biologic level or static time assignment to include the interface of Disease process and organism response (Bechtel, 2017a; Green et al., 2017).

Critical and Theoretical Perspective on Scapular Stabilization: What Does It Really Mean, and Are We on the Right Track?

Stabilization exercises have been a focus and mainstay of many therapeutic and performance training programs in the past decade. Whether the focus is core stabilization for the spine or scapular stabilization, clinicians and trainers alike have endorsed these programs, largely on the basis of conceptual theory and anecdotal experience. The notion that an unstable scapula is related to shoulder dysfunction and pathology is well accepted, but is it accurate? The aim of this perspective article is to challenge the concept of scapular stabilization through the application of biomechanical and motor control constructs. The objectives are to critically examine current beliefs about scapular stabilization, to discuss definitions of stabilization and stability in the context of the scapulothoracic region, and to evaluate key evidence regarding scapular stabilization and scapular dyskinesia. Several new approaches that may affect the understanding of normal and atypical scapula motion are explored. Finally, a historical analogy is presented and future research and clinical directions are suggested. The aims are to lead readers to the essential concepts implied on scapular stabilization, to increase the critical thought process in rehabilitation practice, and to suggest some open topics to be explored in future research.

Improving the physiological realism of experimental models

The Virtual Physiological Human (VPH) project aims to develop integrative, explanatory and predictive computational models (C-Models) as numerical investigational tools to study disease, identify and design effective therapies and provide an in silico platform for drug screening. Ultimately, these models rely on the analysis and integration of experimental data. As such, the success of VPH depends on the availability of physiologically realistic experimental models (E-Models) of human organ function that can be parametrized to test the numerical models. Here, the current state of suitable E-models, ranging from in vitro non-human cell organelles to in vivo human organ systems, is discussed. Specifically, challenges and recent progress in improving the physiological realism of E-models that may benefit the VPH project are highlighted and discussed using examples from the field of research on cardiovascular disease, musculoskeletal disorders, diabetes and Parkinson's disease.

From global to local: exploring the relationship between parameters and behaviors in models of electrical excitability

Models of electrical activity in excitable cells involve nonlinear interactions between many ionic currents. Changing parameters in these models can produce a variety of activity patterns with sometimes unexpected effects. Further more, introducing new currents will have different effects depending on the initial parameter set. In this study we combined global sampling of parameter space and local analysis of representative parameter sets in a pituitary cell model to understand the effects of adding K (+) conductances, which mediate some effects of hormone action on these cells. Global sampling ensured that the effects of introducing K (+) conductances were captured across a wide variety of contexts of model parameters. For each type of K (+) conductance we determined the types of behavioral transition that it evoked. Some transitions were counterintuitive, and may have been missed without the use of global sampling. In general, the wide range of transitions that occurred when the same current was applied to the model cell at different locations in parameter space highlight the challenge of making accurate model predictions in light of cell-to-cell heterogeneity. Finally, we used bifurcation analysis and fast/slow analysis to investigate why specific transitions occur in representative individual models. This approach relies on the use of a graphics processing unit (GPU) to quickly map parameter space to model behavior and identify parameter sets for further analysis. Acceleration with modern low-cost GPUs is particularly well suited to exploring the moderate-sized (5-20) parameter spaces of excitable cell and signaling models.

Raison d'être of insulin resistance: the adjustable threshold hypothesis

The epidemics of obesity and diabetes demand a deeper understanding of insulin resistance, for which the adjustable threshold hypothesis is formed in this paper. To test the hypothesis, mathematical modelling was used to analyse clinical data and to simulate biological processes at both molecular and organismal levels. I found that insulin resistance roots in the thresholds of the cell's bistable response. By assuming heterogeneity of the thresholds, single cells' all-or-none response can collectively produce a graded response at the whole-body level-conforming to existing data. The thresholds have to be adjustable to adapt to extreme conditions. During pregnancy, for example, the thresholds increase consistently to strengthen the mother's insulin resistance to meet the increasing glucose demand of the expanding fetal brain. I also found that hysteresis, a key element of the adjustable threshold hypothesis, can explain reactive hypoglycaemia, which is characteristic of diabetes complications but remains poorly understood. Contrary to the common belief that insulin promotes glucose disposal, the results imply that insulin is the body's 'ration stamp' to restricting glucose utilization by peripheral tissues and that insulin resistance is primarily a well-evolved mechanism. The hypothesis provides an intuitive and dynamical description of the previously formless insulin resistance, which may make the detection of pre-diabetes possible and may shed light on the optimal timing of therapeutic intervention. It also provides valuable clues to defining subtypes of type 2 diabetes that might respond differently to specific prevention and intervention strategies.

Facing the challenge of mammalian neural microcircuits: taking a few breaths may help

Breathing in mammals is a seemingly straightforward behaviour controlled by the brain. A brainstem nucleus called the preBötzinger Complex sits at the core of the neural circuit generating respiratory rhythm. Despite the discovery of this microcircuit almost 25 years ago, the mechanisms controlling breathing remain elusive. Given the apparent simplicity and well-defined nature of regulatory breathing behaviour, the identification of much of the circuitry, and the ability to study breathing in vitro as well as in vivo, many neuroscientists and physiologists are surprised that respiratory rhythm generation is still not well understood. Our view is that conventional rhythmogenic mechanisms involving pacemakers, inhibition or bursting are problematic and that simplifying assumptions commonly made for many vertebrate neural circuits ignore consequential detail. We propose that novel emergent mechanisms govern the generation of respiratory rhythm. That a mammalian function as basic as rhythm generation arises from complex and dynamic molecular, synaptic and neuronal interactions within a diverse neural microcircuit highlights the challenges in understanding neural control of mammalian behaviours, many (considerably) more elaborate than breathing. We suggest that the neural circuit controlling breathing is inimitably tractable and may inspire general strategies for elucidating other neural microcircuits.

Functionality indexes assessed through a simple model of muscle activation, fatigue and recovery

A nonlinear dynamical system is proposed as a qualitative mathematical model with the twofold aim to reasonably describe the force behavior in a fatiguing sub-maximal contraction and to be possibly employed in assessing muscular activation indexes. The model's properties are studied in terms of its equilibria and their stability properties and the existence of the fatigue equilibrium is ensured as the only system's attractor in the feasibility range of the parameters. Suitable mathematical indicators — related to the dynamical properties of resilience and reactivity — are introduced to characterize the asymptotic and the transient system's behavior. The practical impact of the analytical results is elucidated and a connection is established between the introduced mathematical indicators and muscle functionality indexes as rate of force development, task failure time and complete restore time. Experimental validation with handgrip force signal at high load and possible practical applications are also presented.

Models of electrical activity: calibration and prediction testing on the same cell

Mathematical models are increasingly important in biology, and testability is becoming a critical issue. One limitation is that one model simulation tests a parameter set representing one instance of the biological counterpart, whereas biological systems are heterogeneous in their properties and behavior, and a model often is fitted to represent an ideal average. This is also true for models of a cell's electrical activity; even within a narrowly defined population there can be considerable variation in electrophysiological phenotype. Here, we describe a computational experimental approach for parameterizing a model of the electrical activity of a cell in real time. We combine the inexpensive parallel computational power of a programmable graphics processing unit with the flexibility of the dynamic clamp method. The approach involves 1), recording a cell's electrical activity, 2), parameterizing a model to the recording, 3), generating predictions, and 4), testing the predictions on the same cell used for the calibration. We demonstrate the experimental feasibility of our approach using a cell line (GH4C1). These cells are electrically active, and they display tonic spiking or bursting. We use our approach to predict parameter changes that can convert one pattern to the other.

The myth of genetic enhancement

The ongoing revolution in molecular genetics has led many to speculate that one day we will be able to change the expression or phenotype of numerous complex traits to improve ourselves in many different ways. The prospect of genetic enhancements has generated heated controversy, with proponents advocating research and implementation, with caution advised for concerns about justice, and critics tending to see the prospect of genetic enhancements as an assault on human freedom and human nature. Both camps base their arguments on the unquestioned assumption that the science will realize either their dreams or nightmares. In this paper, I show that their beliefs are based upon two fundamental mistakes. First, they are based upon an unwarranted reliance in a genetic determinism that takes for granted that the traits that we might most want to enhance, like intelligence, aggression, shyness, and even athletic ability, can be causally directed by specific genes. In so doing, character descriptions are reified to be concrete and discrete entities, in this case, genes. Second, they have accepted on faith that there is, or will be, a science to translate their hopes or worries into reality when, in fact, that is unlikely to occur because of the irreducible complexity of phenotypic expression.

Optimal homeostasis necessitates bistable control

Bistability is a fundamental phenomenon in nature. In biology, a number of fine properties of bistability have been identified. However, these properties are only consequences of bistability at the physiological level, which do not explain why it had to emerge during evolution. Using optimal homeostasis as the first principle, I find that bistability emerges as an indispensable control mechanism. It is the only solution to a dilemma in glucose homeostasis: high insulin efficiency is required to confer rapidness in plasma glucose clearance, whereas an insulin sparing state is required to guarantee the brain's safety during fasting. The optimality consideration renders a clear correspondence between the molecular and physiological levels. This new perspective can illuminate studies on the twin epidemics of obesity and diabetes and the corresponding intervening strategies. For example, overnutrition and sedentary lifestyle may represent sudden environmental changes that cause the lose of optimality, which may contribute to the marked rise of obesity and diabetes in our generation. Because this bistability result is independent of the parameters of the mathematical model (for which the result is quite general), some other biological systems may also use bistability to control homeostasis.