Abstract:Precision medicine and molecular systems medicine (MSM) are highly utilized and successful approaches to improve understanding, diagnosis, and treatment of many diseases from bench-to-bedside. Especially in the COVID-19 pandemic, molecular techniques and biotechnological innovation have proven to be of utmost importance for rapid developments in disease diagnostics and treatment, including DNA and RNA sequencing technology, treatment with drugs and natural products and vaccine development. The COVID-19 crisis,… Show more
“…In the first step, aspects and questions were elaborated on the basis of the self-organization theory and its bio-psycho-social conception of human beings presented in medicine (Fig. 1 , [ 5 , 6 ]) as well as the theoretical systematizations of psychological pandemic impact by Taylor [ 1 ] and Fitzgerald et al [ 7 ] (Fig. 2 ).…”
Section: Methodsmentioning
confidence: 99%
“… Fig. 1 Systemic bio-psycho-social model of individual being in confrontation with a pandemic (modificated according [ 5 ]) Fig. 2 Structural analyses of psychosocial burdens of the COVID-19 pandemic …”
Since the beginning of the COVID-19 pandemic, much research has been done on the psycho-social consequences, especially for children, adolescents and families. In the long run, there is a large set of quantitative data available. However, these still seem to be not well understood. Theoretical classifications of the evidence also diagnostic tools still seem to be open. This paper elaborates a possible systematisation based on theoretical models of systemic self-organisation theories. This leads to a model for a comprehensive psycho-social child-in-environment diagnostic to map potential problem areas. Such a theoretical framing should enable both: a deeper understanding of the impact of pandemics on young people and hypotheses for intervention strategies in the context of pandemic management as well as in the context of diagnostic-systemic interventions in psycho-social working settings. In the coming months and years, it will be essential to be able to understand and describe psychosocial disabilities that have developed in the context of the pandemic in a differentiated way in order to establish targeted interventions.
“…In the first step, aspects and questions were elaborated on the basis of the self-organization theory and its bio-psycho-social conception of human beings presented in medicine (Fig. 1 , [ 5 , 6 ]) as well as the theoretical systematizations of psychological pandemic impact by Taylor [ 1 ] and Fitzgerald et al [ 7 ] (Fig. 2 ).…”
Section: Methodsmentioning
confidence: 99%
“… Fig. 1 Systemic bio-psycho-social model of individual being in confrontation with a pandemic (modificated according [ 5 ]) Fig. 2 Structural analyses of psychosocial burdens of the COVID-19 pandemic …”
Since the beginning of the COVID-19 pandemic, much research has been done on the psycho-social consequences, especially for children, adolescents and families. In the long run, there is a large set of quantitative data available. However, these still seem to be not well understood. Theoretical classifications of the evidence also diagnostic tools still seem to be open. This paper elaborates a possible systematisation based on theoretical models of systemic self-organisation theories. This leads to a model for a comprehensive psycho-social child-in-environment diagnostic to map potential problem areas. Such a theoretical framing should enable both: a deeper understanding of the impact of pandemics on young people and hypotheses for intervention strategies in the context of pandemic management as well as in the context of diagnostic-systemic interventions in psycho-social working settings. In the coming months and years, it will be essential to be able to understand and describe psychosocial disabilities that have developed in the context of the pandemic in a differentiated way in order to establish targeted interventions.
“…So far, this process lacks the perspective of patients or the civil population towards the implementation of systems medicine approaches in healthcare. Existing studies on current and future challenges (e.g., ethical, legal, technical, medical, and societal issues) for the development of systems medicine primarily reflect expert perspectives [ 17 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. Studies on attitudes of the civil population or of patients, for example, on digitalization, innovative technologies, and predictive tests, focus exclusively on such individual components [ 51 , 52 , 53 , 54 , 55 ].…”
Advances in (bio)medicine and technological innovations make it possible to combine high-dimensional, heterogeneous health data to better understand causes of diseases and make them usable for predictive, preventive, and precision medicine. This study aimed to determine views on and expectations of “systems medicine” from the perspective of citizens and patients in six focus group interviews, all transcribed verbatim and content analyzed. A future vision of the use of systems medicine in healthcare served as a stimulus for the discussion. The results show that although certain aspects of systems medicine were seen positive (e.g., use of smart technology, digitalization, and networking in healthcare), the perceived risks dominated. The high degree of technification was perceived as emotionally burdensome (e.g., reduction of people to their data, loss of control, dehumanization). The risk-benefit balance for the use of risk-prediction models for disease events and trajectories was rated as rather negative. There were normative and ethical concerns about unwanted data use, discrimination, and restriction of fundamental rights. These concerns and needs of citizens and patients must be addressed in policy frameworks and health policy implementation strategies to reduce negative emotions and attitudes toward systems medicine and to take advantage of its opportunities.
“…While approaches remaining on one level only are able to successfully provide physiological insights and while they are usable for practical purposes, including clinical reasoning and epidemiological decision making, the translation between the levels of causality is difficult and, in most cases, virtually impossible [1]. A methodology bridging this gap is highly needed, but previous approaches were unsatisfactory [2].…”
Section: Introductionmentioning
confidence: 99%
“…Feedback loops are fundamental network motifs and information processing structures in living organisms, providing means for homeostatic control of vital parameters including the concentration of key metabolites and important physical properties. Unfortunately, the usual methodology for the mathematical description of feedback loops as linear time-invariant (LTI) systems is not readily compatible with biochemical insights and theories [2]. It is, therefore, impossible to draw conclusions from physicochemical processes to the corresponding response of the organism and vice versa.…”
Feedback loops are among the primary network motifs in living organisms, ensuring survival via homeostatic control of key metabolites and physical properties. However, from a scientific perspective, their characterization is unsatisfactory since the usual modelling methodology is incompatible with the physiological and biochemical basis of metabolic networks. Therefore, any "vertical translation", i.e. the study of the correspondence between molecular and organismal levels of causality, is difficult and in most cases impossible. As a viable solution, we demonstrate an alternative modelling platform for biological feedback loops that is based on key biochemical principles, including mass action law, enzyme kinetics, binding of mediators to transporters and receptors, and basic pharmacological properties. Subsequently, we show how this framework can be used for translating from molecular to systems-level behaviour. Basic elements of the proposed modelling platform include Michaelis-Menten kinetics defining nonlinear dependence of the output y(t) on an input signal x(t) with the Hill-Langmuir equation y(t) = G * x(t)n / (D + x(t)n), non-competitive inhibition for linking stimulatory and inhibitory inputs with y(t) = G + x1(t) / ((D + x1(t) * (1 + x2(t) / KI)) and processing structures for distribution and elimination. Depending on the structure of the feedback loop, its equifinal (steady-state) behaviour can be solved in form of polynomials, with a quadratic equation for the simplest case with one feedback loop and a Hill exponent of 1, and higher-grade polynomials for additional feedback loops and/or integer Hill exponents > 1. As a companion to the analytical solution, a flexible class library (CyberUnits) facilitates computer simulations for studying the transitional behaviour of the feedback loop. Unlike other modelling strategies in biocybernetics and systems biology, this platform allows for straightforward translation from the statistical properties of single molecules on a "microscopic" level to the behaviour of the whole feedback loop on an organismal "macroscopic" level. An example is the Michaelis constant D, which is equivalent to (k-1 + k2) / k1, where k1, k-1 and k2 denote the rate constants for the association and dissociation of the enzyme-substrate or receptor-hormone complex, respectively. From the perspective of a single molecule the rate constants represent the probability (per unit time) that the corresponding reaction will happen in the subsequent time interval. Therefore 1/k represents the mean lifetime of the complex. Very similar considerations apply to the other described constants of the feedback loop. In summary, this modelling technique renders the translation from a molecular level to a systems perspective possible. In addition to providing new insights into the physiology of biological feedback loops, it may be a valuable tool for multiple disciplines of biomedical research, including drug design, molecular genetics and investigations on the effects of endocrine disruptors.
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