Interacting Feedback Loops Research Articles

Ethnokinesiology: towards a neuromechanical understanding of cultural differences in movement.

Each individual's movements are sculpted by constant interactions between sensorimotor and sociocultural factors. A theoretical framework grounded in motor control mechanisms articulating how sociocultural and biological signals converge to shape movement is currently missing. Here, we propose a framework for the emerging field of ethnokinesiology aiming to provide a conceptual space and vocabulary to help bring together researchers at this intersection. We offer a first-level schema for generating and testing hypotheses about cultural differences in movement to bridge gaps between the rich observations of cross-cultural movement variations and neurophysiological and biomechanical accounts of movement. We explicitly dissociate two interacting feedback loops that determine culturally relevant movement: one governing sensorimotor tasks regulated by neural signals internal to the body, the other governing ecological tasks generated through actions in the environment producing ecological consequences. A key idea is the emergence of individual-specific and culturally influenced motor concepts in the nervous system, low-dimensional functional mappings between sensorimotor and ecological task spaces. Motor accents arise from perceived differences in motor concept topologies across cultural contexts. We apply the framework to three examples: speech, gait and grasp. Finally, we discuss how ethnokinesiological studies may inform personalized motor skill training and rehabilitation, and challenges moving forward.This article is part of the theme issue 'Minds in movement: embodied cognition in the age of artificial intelligence'.

A socio-technical transition perspective on positive tipping points in climate change mitigation: Analysing seven interacting feedback loops in offshore wind and electric vehicles acceleration

This paper engages with climate mitigation debates on positive tipping points, which attract increasing attention but remain divided between technological and social tipping point approaches. Building on recent attempts to overcome this dichotomy, the paper develops a socio-technical transitions perspective which shows how co-evolutionary interactions between techno-economic improvements and actor reorientations can significantly accelerate diffusion. Mobilising insights from political science, discourse theory, business studies, consumption theory, and innovation studies, we elaborate the Multi-Level Perspective to articulate seven feedback loops in tipping point dynamics. We illustrate and test our co-evolutionary perspective with two case studies, UK offshore wind and electric vehicles. These case studies not only demonstrate the importance of interacting feedback loops, but also show a contrasting sequence in tipping point dynamics, with substantial techno-economic deployment preceding major actor reorientations in offshore wind, while following them in the EV case. The cases also indicate the crucial roles of policymakers in low-carbon tipping point dynamics as well as the importance of policy learning and social, political, and business feedbacks in strengthening and reorienting policy support.

The ‘dark side’ of data–driven marketing: a system’s thinking analysis

ABSTRACT Data-driven marketing has become a prominent strategic marketing approach, but it is prone to complex challenges, often referred as its dark side. The literature on data-driven marketing is dominated by its positive aspects, but there is an emerging literature stream focussing on its challenges. This study aims at contributing to this literature stream by providing an empirical study involving a holistic analysis of the undesirable effects of data-driven marketing using a systems thinking approach. The challenges faced by a New Zealand university in its international marketing using data-driven marketing is used as a case. Using 23 interviews, a systems model was developed, capturing the underlying structure behind these challenges. Eight interacting feedback loops were identified in the model, responsible for these complex challenges. A focus group was used to formulate three strategic interventions to address the complex challenges facing this organisation involved in data-driven marketing.

Transient Antibiotic Tolerance Triggered by Nutrient Shifts From Gluconeogenic Carbon Sources to Fatty Acid.

Nutrient shifts from glycolytic-to-gluconeogenic carbon sources can create large sub-populations of extremely antibiotic tolerant bacteria, called persisters. Positive feedback in Escherichia coli central metabolism was believed to play a key role in the formation of persister cells. To examine whether positive feedback in nutrient transport can also support high persistence to β-lactams, we performed nutrient shifts for E. coli from gluconeogenic carbon sources to fatty acid (FA). We observed tri-phasic antibiotic killing kinetics characterized by a transient period of high antibiotic tolerance, followed by rapid killing then a slower persister-killing phase. The duration of transient tolerance (3–44 h) varies with pre-shift carbon source and correlates strongly with the time needed to accumulate the FA degradation enzyme FadD after the shift. Additionally, FadD accumulation time and thus transient tolerance time can be reduced by induction of the glyoxylate bypass prior to switching, highlighting that two interacting feedback loops simultaneously control the length of transient tolerance. Our results demonstrate that nutrient switches along with positive feedback are not sufficient to trigger persistence in a majority of the population but instead triggers only a temporary tolerance. Additionally, our results demonstrate that the pre-shift metabolic state determines the duration of transient tolerance and that supplying glyoxylate can facilitate antibiotic killing of bacteria.

Neuroscientific therapies for atrial fibrillation.

The cardiac autonomic nervous system (ANS) plays an integral role in normal cardiac physiology as well as in disease states that cause cardiac arrhythmias. The cardiac ANS, comprised of a complex neural hierarchy in a nested series of interacting feedback loops, regulates atrial electrophysiology and is itself susceptible to remodelling by atrial rhythm. In light of the challenges of treating atrial fibrillation (AF) with conventional pharmacologic and myoablative techniques, increasingly interest has begun to focus on targeting the cardiac neuraxis for AF. Strong evidence from animal models and clinical patients demonstrates that parasympathetic and sympathetic activity within this neuraxis may trigger AF, and the ANS may either induce atrial remodelling or undergo remodelling itself to serve as a substrate for AF. Multiple nexus points within the cardiac neuraxis are therapeutic targets, and neuroablative and neuromodulatory therapies for AF include ganglionated plexus ablation, epicardial botulinum toxin injection, vagal nerve (tragus) stimulation, renal denervation, stellate ganglion block/resection, baroreceptor activation therapy, and spinal cord stimulation. Pre-clinical and clinical studies on these modalities have had promising results and are reviewed here.

Cardiac Neuroanatomy for the Cardiac Electrophysiologist.

The cardiac neuraxis is integral to cardiac physiology, and its dysregulation is implicated in cardiovascular disease. Neuromodulatory therapies are being developed that target the cardiac autonomic nervous system (ANS) to treat cardiac pathophysiology. An appreciation of the cardiac neuroanatomy is a prerequisite for development of such targeted therapies. Here, we provide a review of the current understanding of the cardiac ANS. The parasympathetic and sympathetic nervous system are composed of higher order cortical centers, brainstem, spinal cord, intrathoracic extracardiac ganglia and intrinsic cardiac ganglia. A series of interacting feedback loops mediates reflex pathways to exert control over the cardiac conduction system and contractile tissue. Further exploration of this complex regulatory system promises to yield neuroscience-based therapeutics for cardiac disease.

Translational control of gene expression via interacting feedback loops.

Translation is a key step in the synthesis of proteins. Accordingly, cells have evolved an intricate array of control mechanisms to regulate this process. By constructing a multicomponent mathematical framework we uncover how translation may be controlled via interacting feedback loops. Our results reveal that this interplay gives rise to a remarkable range of protein synthesis dynamics, including oscillations, step change, and bistability. This suggests that cells may have recourse to a much richer set of control mechanisms than was previously understood.

Quantifying Drug-Induced Bone Marrow Toxicity Using a Novel Haematopoiesis Systems Pharmacology Model.

Haematological toxicity associated with cancer therapeutics is monitored by changes in blood cell count, and their primary effect is on proliferative progenitors in the bone marrow. Using observations in rat bone marrow and blood, we characterize a mathematical model that comprises cell proliferation and differentiation of the full haematopoietic phylogeny, with interacting feedback loops between lineages in homeostasis as well as following carboplatin exposure. We accurately predicted the temporal dynamics of several mature cell types related to carboplatin‐induced bone marrow toxicity and identified novel insights into haematopoiesis. Our model confirms a significant degree of plasticity within bone marrow cells, with the number and type of both early progenitors and circulating cells affecting cell balance, via feedback mechanisms, through fate decisions of the multipotent progenitors. We also demonstrated cross‐species translation of our predictions to patients, applying the same core model structure and considering differences in drug‐dependent and physiology‐dependent parameters.

A System Dynamics Model of Resistance to Organizational Change: The Role of Participatory Strategies

The literature on organizational change and resistance to change is contradictory. Some scholars find resistance a hindrance to successful change (traditional paradigm), whereas others find it a valuable source (modern paradigm). The objective of this study is to enhance our understanding of how resistance affects organizational change by providing a coherent system dynamics perspective. Based on interviews, expert modelling and group modelling, this inductive case study develops a causal loop diagram that displays eight interacting feedback loops to explain resistance to change and the role that participatory strategies play in addressing this. The model contributes to the theoretical debate on how resistance affects change by providing propositions that integrate the traditional and modern paradigms. When managers face decisions about when to increase, stabilize or decrease the use of participatory strategies, our findings imply to base these decisions upon currently dominating feedback loops, such as the Stress Trap or Slow Trap. © 2018 The Authors. Systems Research and Behavioral Science published by International Federation for Systems Research and John Wiley & Sons Ltd

A Systems Dynamic Approach to Alzheimer’s Disease Prevention

ABSTRACT
 ObjectivesAs estimated there are about 5.3 million who suffer from Alzheimer’s disease in United States. The incidence is increasing as the population is aging. Due to the increasing trend of Alzheimer’s disease, there is a lot of discussion on prevention efforts or slowing the incidence. Also, models that could predict individual risk of cognitive impairment are needed to assist in prevention efforts. 
 In general dementia development has been associated with growth in various vascular, lifestyle and other risk factors. Epidemiological research provides evidence of some vascular, lifestyle and psychological risk factors that are modifiable and protective of disease incidence either independently or while interacting with other factors. However, as reported by National Institute of Aging, it is not yet clear whether health or lifestyle factors can prevent Alzheimer’s disease.
 The objective of this research project is to adopt a system dynamics modeling approach to study the interaction of several key factors including vascular, lifestyle and psychological aspects over the life course of individuals, to gain further understanding of Alzheimer’s disease incidence and evaluate prevention strategies. Both datasets of ‘Alzheimer's Disease Neuroimaging Initiative (ADNI)’ and ‘Health and Retirement Study (HRS)’ will be used for model development and validation.
 ApproachA system dynamics approach is an optimal choice for addressing the goal of this proposal because different key factors interact over time and make Alzheimer’s disease incidence a complex problem. Furthermore, system dynamics approaches focus on understanding the relationship between the structure of a system and the resulting dynamic behaviors generated through multiple interacting feedback loops. Such an approach could be invaluable in studying dynamic problems arising in complex health, social, economic, or ecological systems.
 ResultsFor the purpose of the proposal, the following stages are planned:1. Develop a system dynamics simulation model at individual level that predicts the Alzheimer’s disease incidence over the life course, and aggregates individual level models to predict population level trends
 2. Calibrate the resulting simulation model based upon longitudinal data trends employed from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Both cohorts with Alzheimer’s disease and control subjects from this database will be used to fine-tune the simulation model.
 ConclusionThe final validated model would be used to test different hypotheses and evaluate various strategies and/or their combinations to help evaluate the efficacy of prevention strategies on Alzheimer’s disease incidence and its growth.

Cardiac innervation and sudden cardiac death.

Afferent and efferent cardiac neurotransmission via the cardiac nerves intricately modulates nearly all physiological functions of the heart (chronotropy, dromotropy, lusitropy, and inotropy). Afferent information from the heart is transmitted to higher levels of the nervous system for processing (intrinsic cardiac nervous system, extracardiac-intrathoracic ganglia, spinal cord, brain stem, and higher centers), which ultimately results in efferent cardiomotor neural impulses (via the sympathetic and parasympathetic nerves). This system forms interacting feedback loops that provide physiological stability for maintaining normal rhythm and life-sustaining circulation. This system also ensures that there is fine-tuned regulation of sympathetic-parasympathetic balance in the heart under normal and stressed states in the short (beat to beat), intermediate (minutes to hours), and long term (days to years). This important neurovisceral/autonomic nervous system also plays a major role in the pathophysiology and progression of heart disease, including heart failure and arrhythmias leading to sudden cardiac death. Transdifferentiation of neurons in heart failure, functional denervation, cardiac and extracardiac neural remodeling has also been identified and characterized during the progression of disease. Recent advances in understanding the cellular and molecular processes governing innervation and the functional control of the myocardium in health and disease provide a rational mechanistic basis for the development of neuraxial therapies for preventing sudden cardiac death and other arrhythmias. Advances in cellular, molecular, and bioengineering realms have underscored the emergence of this area as an important avenue of scientific inquiry and therapeutic intervention.

A computational model of insect discontinuous gas exchange: A two-sensor, control systems approach.

The insect gas exchange system is characterised by branching air-filled tubes (tracheae/tracheoles) and valve-like structures in their outer integument (spiracles) which allow for a periodic gas exchange pattern known as the discontinuous gas exchange cycle (DGC). The DGC facilitates the temporal decoupling of whole animal gas exchange from cellular respiration rates and may confer several physiological benefits, which are nevertheless highly controversial (primarily reduction of cellular oxidative damage and/or respiratory water saving). The intrinsic and extrinsic factors influencing DGCs are the focus of extensive ongoing research and little consensus has been reached on the evolutionary genesis or mechanistic costs and benefits of the pattern. Despite several hypotheses and much experimental and evolutionary biology research, a mechanistic physical model, which captures various key elements of the DGC pattern, is currently lacking. Here, we present a biologically realistic computational, two-sensor DGC model (pH/carbon dioxide and oxygen setpoints) for an Orthopteran gas exchange system, and show computationally for the first time that a control system of two interacting feedback loops is capable of generating a full DGC pattern with outputs which are physiologically realistic, quantitatively matching experimental results found in this taxonomic model elsewhere. A finite-element mathematical approach is employed and various trigger sets are considered. Parameter sensitivity analyses suggest that various aspects of insect DGC are adequately captured in this model. In particular, with physiologically relevant input parameters, the full DGC pattern is induced; and the phase durations, endotracheal carbon dioxide partial pressure ranges, and pH fluctuations which arise are physically realistic. The model results support the emergent property hypothesis for the existence of DGC, and indicate that asymmetric loading and off-loading (hysteresis) in one of the sensor feedback loops, which is a novel inclusion here, is a critical aspect of the insect spiracle-tracheal gas exchange system.

Engineering and implementing software architectural patterns based on feedback loops

A highly decentralized system of autonomous service components consists of multiple and interacting feedback loops which can be organized into a variety of architectural patterns. The highly complex nature of these loops make engineering and implementation of these patterns a very challenging task. In this paper, we present SimSOTA - an integrated Eclipse plug-in to architect, engineer and implement self-adaptive systems based on our feedback loop-based approach. SimSOTA adopts model-driven development to model and simulate complex self-adaptive architectural patterns, and to automate the generation of Java-based implementation code. The approach is validated using a case study in cooperative electric vehicles.

Temperature-dependent variation in gas exchange patterns and spiracular control in Rhodnius prolixus.

Insects display an array of respiratory behaviors, including the use of discontinuous gas exchange. This pattern is characterized by periods of spiracular closure, micro-openings (flutter), and complete openings during which the majority of gas exchange takes place. A current model of insect spiracular control suggests that spiracles are controlled by two interacting feedback loops, which produce the discontinuous pattern. The flutter period is thought to be initiated by a critically low partial pressure of oxygen, while the open period is initiated by a critically high CO2 threshold. The goal of our study was to test this control model under conditions of feeding-induced or temperature-induced changes in metabolic rate. We manipulated the metabolic rate of the insect Rhodnius prolixus using two discrete mechanisms: (1) feeding the insects a bloodmeal or (2) exposing them to a range of temperatures (18-38°C). Examining the variation in the gas exchange patterns produced by insects in each of these treatments allowed us to determine whether spiracular control is sensitive to metabolic rate and/or temperature. We found that increases in temperature caused significant decreases in open phase burst volumes and premature abandonment of discontinuous gas exchange cycles. These effects were not observed in fed individuals maintained at a single temperature despite their higher metabolic rates. Our results indicate that some part of the spiracular control mechanism is temperature sensitive, suggesting a possible role for pH in CO2 sensing.

Control of breathing by interacting pontine and pulmonary feedback loops

The medullary respiratory network generates respiratory rhythm via sequential phase switching, which in turn is controlled by multiple feedbacks including those from the pons and nucleus tractus solitarii; the latter mediates pulmonary afferent feedback to the medullary circuits. It is hypothesized that both pontine and pulmonary feedback pathways operate via activation of medullary respiratory neurons that are critically involved in phase switching. Moreover, the pontine and pulmonary control loops interact, so that pulmonary afferents control the gain of pontine influence of the respiratory pattern. We used an established computational model of the respiratory network [1] and extended it by incorporating pontine circuits and pulmonary feedback (Figure ​(Figure1).1). In the extended model, the pontine neurons receive phasic excitatory activation from, and provide feedback to, medullary respiratory neurons responsible for the onset and termination of inspiration. The model was used to study the effects of: (1) (removal of pulmonary feedback), (2) suppression of pontine activity attenuating pontine feedback, and (3) these perturbations applied together on the respiratory pattern and durations of inspiration (TI) and expiration (TE). Figure 1 A general schematic diagram representing the system with two interacting feedback loops. In our model: (a) the simulated vagotomy resulted in increases of both TI and TE, (b) the suppression of pontine-medullary interactions led to the prolongation of TI at relatively constant, but variable TE, and (c) these perturbations applied together resulted in apneusis, characterized by a significantly prolonged TI. The results of modeling were compared with, and provided a reasonable explanation for, multiple experimental data. The model was able to reproduce the experimentally demonstrated changes in TI and TE and phrenic pattern following vagotomy and/or pontine suppression by NMDA receptor blockers MK801and AP-5. According to the model these changes reflect the characteristic changes in the balance between the pontine and pulmonary feedback mechanisms involved in control of breathing during various cardio-respiratory disorders and diseases.

Temperature dependent variation in respiratory patterns and spiracular control in Rhodnius prolixus

Our understanding of insect respiratory control indicates that spiracular activity is regulated by two interacting feedback loops which respond to changes in internal pCO2 and pO2. Since spiracles open in response to a specific pCO2 threshold, the volume of CO2 released in a burst by a discontinuously respiring insect should remain constant. However, previous studies which manipulated metabolic rate via temperature found that burst volume decreases at high temperatures. We used Rhodnius prolixus to test if this variation is caused by changes in metabolic rate or by an effect of temperature on spiracular control. We increased metabolic rate by either increasing ambient temperature or feeding a bloodmeal. Burst volume decreased significantly as temperature increased from 18°C to 38°C (ANOVA, F=89.58, p<0.001) but showed no relationship to metabolic rate in fed animals measured at 24°C (BV= −0.0016MR + 0.0243, R2=0.0015). At high temperatures, insects abandoned discontinuous respiration at lower metabolic rates than fed insects. It is clear that insect respiratory patterns are influenced by both temperature and metabolic rate. Support provided by NSF IOS‐0920683 (TJB).

Financially sustainable management strategies for urban wastewater collection infrastructure – development of a system dynamics model

Causal loop diagrams are developed for wastewater collection networks to identify complex interactions and feedback loops among physical, financial, and social sectors. Causal loop diagrams are then incorporated into a novel system dynamics based decision support tool that can be used for financially sustainable management of wastewater collection networks. Data requirements to develop the decision support tool are discussed along with how can the decision support tool be used to manage a utility.The presented causal loop diagram is the first known attempt to lay out the interrelationships among system components using a formal technique. The causal loop diagram establishes the existence of several interacting feedback loops and demonstrates that the management of wastewater collection networks constitutes a complex dynamic system for which traditional management tools are deemed inadequate. The use of causal loop diagrams can be useful to mitigate effects of the silo-based organizational culture prevalent in many water utilities.The system dynamics model is the first known decision support tool to quantitatively simulate the influence of interrelationships and feedback loops in wastewater collection network management. The model is a mathematical representation of the causal loop diagram to capture cost drivers and revenues sources in the system. It also includes a set of policy levers which allows formulation of various financing and rehabilitation strategies. The model can be used to develop short- and long-term management plans. The impact of financing and rehabilitation strategies on system performance can be simulated and evaluated in terms of financial and service level metrics. The decision support tool can also be used by utilities to ensure essential data is collected and flows within organizational units.

Component-Based Modeling and Reachability Analysis of Genetic Networks

Genetic regulatory networks usually encompass a multitude of complex, interacting feedback loops. Being able to model and analyze their behavior is crucial for understanding their function. However, state space explosion is becoming a limiting factor in the formal analysis of genetic networks. This paper explores a modular approach for verification of reachability properties. A framework for component-based modeling of genetic regulatory networks, based on a modular discrete abstraction, is introduced. Then a compositional algorithm to efficiently analyze reachability properties of the model is proposed. A case study on embryonic cell differentiation involving several hundred cells shows the potential of this approach.

Partners in Time: EARLY BIRD Associates with ZEITLUPE and Regulates the Speed of the Arabidopsis Clock

The circadian clock of the model plant Arabidopsis (Arabidopsis thaliana) is made up of a complex series of interacting feedback loops whereby proteins regulate their own expression across day and night. early bird (ebi) is a circadian mutation that causes the clock to speed up: ebi plants have short circadian periods, early phase of clock gene expression, and are early flowering. We show that EBI associates with ZEITLUPE (ZTL), known to act in the plant clock as a posttranslational mediator of protein degradation. However, EBI is not degraded by its interaction with ZTL. Instead, ZTL counteracts the effect of EBI during the day and increases it at night, modulating the expression of key circadian components. The partnership of EBI with ZTL reveals a novel mechanism involved in controlling the complex transcription-translation feedback loops of the clock. This work highlights the importance of cross talk between the ubiquitination pathway and transcriptional control for regulation of the plant clock.

Medical Process Modeling with a Hybrid System Dynamics Zachman Framework

Diagnoses are perhaps the most complex and crucial decisions within the modern healthcare enterprise. Models for complex healthcare decisions must incorporate consideration for the usual multiplicity of important factors, interacting feedback loops among these factors, and the dynamic nature of the full diagnostic arena. A diagnoses modeling technique that has the requisite variety of relevant considerations is presented. The technique has the potential to overcome mandatory time criteria, while considering the competence and robustness of high importance diagnostic decisions. In this study, descriptive narratives dictated by examining physicians who were directly involved in the diagnosis and treatment of patients were examined in detail not only to extract key factors involved in medical decision making processes, but also to illustrate the wide ontological origin of key decision making factors. Important factors in the narratives were identified and mapped with a new System Dynamics methodology that incorporates a Zachman Framework for establishing the overall scope and context of the full medical decision making context within the modern medical enterprise. The two techniques produce a synergy that addresses the debilities of the techniques in isolation, allowing enhanced comprehension of diagnostic processes, and their improvement.