Rethinking the pragmatic systems biology and systems-theoretical biology divide: Toward a complexity-inspired epistemology of systems biomedicine

Classifying hepatitis B therapies with insights from covalently closed circular DNA dynamics

The achievement of a functional cure for chronic hepatitis B (CHB) remains limited to a minority of patients treated with currently approved drugs. The primary objective in developing new anti-HBV drugs is to enhance the functional cure rates for CHB. A critical prerequisite for the functional cure of CHB is a substantial reduction, or even eradication of covalently closed circular DNA (cccDNA). Within this context, the changes in cccDNA levels during treatment become as a pivotal concern. We have previously analyzed the factors influencing cccDNA dynamics and introduced a preliminary classification of hepatitis B treatment strategies based on these dynamics. In this review, we employ a systems thinking perspective to elucidate the fundamental aspects of the HBV replication cycle and to rationalize the classification of treatment strategies according to their impact on the dynamic equilibrium of cccDNA. Building upon this foundation, we categorize current anti-HBV strategies into two distinct groups and advocate for their combined use to significantly reduce cccDNA levels within a well-defined timeframe.

Molecular Insights into Transcranial Direct Current Stimulation Effects: Metabolomics and Transcriptomics Analyses

INTRODUCTION

Transcranial direct current stimulation (tDCS) is an evolving non-invasive neurostimulation technique. Despite multiple studies, its underlying molecular mechanisms are still unclear. Several previous human studies of the effect of tDCS suggest that it generates metabolic effects. The induction of metabolic effects by tDCS could provide an explanation for how it generates its long-term beneficial clinical outcome.

AIM

Given these hints of tDCS metabolic effects, we aimed to delineate the metabolic pathways involved in its mode of action.

METHODS

To accomplish this, we utilized a broad analytical approach of co-analyzing metabolomics and transcriptomic data generated from anodal tDCS in rat models. Since no metabolomic dataset was available, we performed a tDCS experiment of bilateral anodal stimulation of 200 µA for 20 min and for 5 consecutive days, followed by harvesting the brain tissue below the stimulating electrode and generating a metabolomics dataset using LC-MS/MS. The analysis of the transcriptomic dataset was based on a publicly available dataset.

RESULTS

Our analyses revealed that tDCS alters the metabolic profile of brain tissue, affecting bioenergetic-related pathways, such as glycolysis and mitochondrial functioning. In addition, we found changes in calcium-related signaling.

CONCLUSIONS

We conclude that tDCS affects metabolism by modulating energy production-related processes. Given our findings concerning calcium-related signaling, we suggest that the immediate effects of tDCS on calcium dynamics drive modifications in distinct metabolic pathways. A thorough understanding of the underlying molecular mechanisms of tDCS has the potential to revolutionize its applicability, enabling the generation of personalized medicine in the field of neurostimulation and thus contributing to its optimization.

A Pragmatic and Systemic Approach to Advance Research in Health Policy and Management Comment on "Insights Gained From a Re-analysis of Five Improvement Cases in Healthcare Integrating System Dynamics Into Action Research"

"Healthcare is complex" - or similar sentences - is a statement that introduces a wide number of scientific articles dealing with health policy and management issues. We all agree that healthcare is complex, but most studies, although using this kind of sentence to introduce their background, do little to effectively deal with such complexity in their analyses. Holmström et al proposed a methodological approach to tackle healthcare complexity by integrating system dynamics (SD) into action research (AR). This commentary highlights three touch points that makes the combination of AR and SD feasible, namely the epistemological ground, the use of experimentation and the collaborative approach. The proposed approach addresses some of the key sources of the complexity characterizing healthcare settings.

Toward a More General Understanding of Bohr's Complementarity: Insights from Modeling of Ion Channels

Some contemporary theorists such as Mazzocchi, Theise and Kafatos are convinced that the reformed complementarity may redefine how we might exploit the complexity theory in 21st-century life sciences research. However, the motives behind the profound re-invention of "biological complementarity" need to be substantiated with concrete shreds of evidence about this principle's applicability in real-life science experimentation, which we found missing in the literature. This paper discusses such pieces of evidence by confronting Bohr's complementarity and ion channel modeling practice. We examine whether and to what extent this principle might assist in developing ion channel models incorporating both deterministic and stochastic solutions. According to the "mutual exclusiveness of experimental setups" version of Bohr's complementarity, this principle is needed when two mutually exclusive perspectives or approaches are right, necessary in a particular context, and are not contradictory as they arise in mutually exclusive conditions (mutually exclusive experimental or modeling setups). A detailed examination of the modeling practice reveals that both solutions are often used simultaneously in a single ion channel model, suggesting that the opposite conceptual frameworks can coexist in the same modeling setup. We concluded that Bohr's complementarity might find applications in these complex modeling setups but only through its realistic phenomenological interpretation that allows applying different modes of description regardless of the nature of the underlying ion channel opening process. Also, we propose the combined use of complementarity and Complex thinking in building the multifaceted ion channel models. Overall, this paper's results support the efforts to establish a more general form of complementarity to meet today's complexity theory-inspired life sciences modeling demands.