Complexity and the reductionism-holism debate in systems biology

The "plant neurobiology" revolution

The 21st-century "plant neurobiology" movement is an amalgam of scholars interested in how "neural processes", broadly defined, lead to changes in plant behavior. Integral to the movement (now called plant behavioral biology) is a triad of historically marginalized subdisciplines, namely plant ethology, whole plant electrophysiology and plant comparative psychology, that set plant neurobiology apart from the mainstream. A central tenet held by these "triad disciplines" is that plants are exquisitely sensitive to environmental perturbations and that destructive experimental manipulations rapidly and profoundly affect plant function. Since destructive measurements have been the norm in plant physiology, much of our "textbook knowledge" concerning plant physiology is unrelated to normal plant function. As such, scientists in the triad disciplines favor a more natural and holistic approach toward understanding plant function. By examining the history, philosophy, sociology and psychology of the triad disciplines, this paper refutes in eight ways the criticism that plant neurobiology presents nothing new, and that the topics of plant neurobiology fall squarely under the purview of mainstream plant physiology. It is argued that although the triad disciplines and mainstream plant physiology share the common goal of understanding plant function, they are distinct in having their own intellectual histories and epistemologies.

Symbioses between fungi and bacteria: from mechanisms to impacts on biodiversity

Symbiotic interactions between fungi and bacteria range from positive to negative. They are ubiquitous in free-living as well as host-associated microbial communities worldwide. Yet, the impact of fungal-bacterial symbioses on the organization and dynamics of microbial communities is uncertain. There are two reasons for this uncertainty: (1) knowledge gaps in the understanding of the genetic mechanisms underpinning fungal-bacterial symbioses and (2) prevailing interpretations of ecological theory that favor antagonistic interactions as drivers stabilizing biological communities despite the existence of models emphasizing contributions of positive interactions. This review synthesizes information on fungal-bacterial symbioses common in the free-living microbial communities of the soil as well as in host-associated polymicrobial biofilms. The interdomain partnerships are considered in the context of the relevant community ecology models, which are discussed critically.

Systematic analysis and optimization of early warning signals for critical transitions using distribution data

Abrupt shifts between alternative regimes occur in complex systems, from cell regulation to brain functions to ecosystems. Several model-free early warning signals (EWS) have been proposed to detect impending transitions, but failure or poor performance in some systems have called for better investigation of their generic applicability. Notably, there are still ongoing debates whether such signals can be successfully extracted from data in particular from biological experiments. In this work, we systematically investigate properties and performance of dynamical EWS in different deteriorating conditions, and we propose an optimized combination to trigger warnings as early as possible, eventually verified on experimental data from microbiological populations. Our results explain discrepancies observed in the literature between warning signs extracted from simulated models and from real data, provide guidance for EWS selection based on desired systems and suggest an optimized composite indicator to alert for impending critical transitions using distribution data.

Making sense of complexity: A qualitative 'Framework' analysis of naturopathic case management and clinical reasoning

BACKGROUND AND PURPOSE

The clinical encounter is inherently complex and uncertain. Naturopathic clinical practice is shaped by a traditional philosophy and practice guiding principles, with a therapeutic framework that incorporates a complex inter-systems approach. It is possible that this foundation may orient naturopathic practitioners to manage clinical complexity and uncertainty in a distinct manner. The aim of this study is to explore the perceptions of experienced naturopathic practitioners to the management of clinical complexity within naturopathic care.

MATERIALS AND METHODS

Twenty experienced Australian naturopathic practitioners participated across four focus groups, responding to semi-structured questions regarding their clinical reasoning strategies and case management processes. The data were analysed using a seven step Framework analysis method.

RESULTS

Three primary themes were identified: i) patient is encountered as a whole entity, ii) clinical reasoning is ampliative and explicative, and iii) treatment reflects systems thinking. Participants perceived a focus on the connections between various case elements, building a comprehensive internalised schematic of each case. Participants saw treatments as meeting various needs including prevention, symptom alleviation, causal mitigation, and support of innate healing processes.

CONCLUSION

Naturopathic practitioners perceive they clinically embody the traditional holistic philosophy of naturopathy as a systems orientation, incorporating traditional and contemporary bioscience knowledge. This appears to shape a distinct naturopathic case management approach, oriented to working with patients in a complexity-informed manner.

Network analysis to evaluate complexities in relationships among fermentation variables measured within continuous culture experiments

The objective of this study was to leverage a frequentist (ELN) and Bayesian learning (BLN) network analyses to summarize quantitative associations among variables measured in 4 previously published dual-flow continuous culture fermentation experiments. Experiments were originally designed to evaluate effects of nitrate, defaunation, yeast, and/or physiological shifts associated with pH or solids passage rates on rumen conditions. Measurements from these experiments that were used as nodes within the networks included concentrations of individual volatile fatty acids, mM and nitrate, NO3-,%; outflows of non-ammonia nitrogen (NAN, g/d), bacterial N (BN, g/d), residual N (RN, g/d), and ammonia N (NH3-N, mg/dL); degradability of neutral detergent fiber (NDFd, %) and degradability of organic matter (OMd, %); dry matter intake (DMI, kg/d); urea in buffer (%); fluid passage rate (FF, L/d); total protozoa count (PZ, cells/mL); and methane production (CH4, mmol/d). A frequentist network (ELN) derived using a graphical LASSO (least absolute shrinkage and selection operator) technique with tuning parameters selected by Extended Bayesian Information Criteria (EBIC) and a BLN were constructed from these data. The illustrated associations in the ELN were unidirectional yet assisted in identifying prominent relationships within the rumen that were largely consistent with current understanding of fermentation mechanisms. Another advantage of the ELN approach was that it focused on understanding the role of individual nodes within the network. Such understanding may be critical in exploring candidates for biomarkers, indicator variables, model targets, or other measurement-focused explorations. As an example, acetate was highly central in the network suggesting it may be a strong candidate as a rumen biomarker. Alternatively, the major advantage of the BLN was its unique ability to imply causal directionality in relationships. Because the BLN identified directional, cascading relationships, this analytics approach was uniquely suited to exploring the edges within the network as a strategy to direct future work researching mechanisms of fermentation. For example, in the BLN acetate responded to treatment conditions such as the source of N used and the quantity of substrate provided, while acetate drove changes in the protozoal populations, non-NH3-N and residual N flows. In conclusion, the analyses exhibit complementary strengths in supporting inference on the connectedness and directionality of quantitative associations among fermentation variables that may be useful in driving future studies.

Data structuring may prevent ambiguity and improve personalized medical prognosis

Topics expected to influence personalized medicine (PM), where medical decisions, practices, and treatments are tailored to the individual patient, are reviewed. Lack of discrimination due to different biological conditions that express similar values of numerical variables (ambiguity) is regarded to be a major potential barrier for PM. This material explores possible causes and sources of ambiguity and offers suggestions for mitigating the impacts of uncertainties. Three causes of ambiguity are identified: (1) delayed adoption of innovations, (2) inadequate emphases, and (3) inadequate processes used when new medical practices are developed and validated. One example of the first problem is the relative lack of medical research on "compositional data" -the type that characterizes leukocyte data. This omission results in erroneous use of data abundantly utilized in medicine, such as the blood cell differential. Emphasis on data output ‒not biomedical interpretation that facilitates the use of clinical data‒ exemplifies the second type of problems. Reliance on tools generated in other fields (but not validated within biomedical contexts) describes the last limitation. Because reductionism is associated with these problems, non-reductionist alternatives are reviewed as potential remedies. Data structuring (converting data into information) is considered a key element that may promote PM. To illustrate a process that includes data-information-knowledge and decision-making, previously published data on COVID-19 are utilized. It is suggested that ambiguity may be prevented or ameliorated. Provided that validations are grounded on biomedical knowledge, approaches that describe certain criteria - such as non-overlapping data intervals of patients that experience different outcomes, immunologically interpretable data, and distinct graphic patterns - can inform, at personalized bases, earlier and/or with fewer observations.

The converging paradigms of holism and complexity: An exploration of naturopathic clinical case management using complexity science principles

RATIONALE

Traditional whole systems of medicine, such as naturopathy, are founded upon holism; a philosophical paradigm consistent with contemporary complexity science. Naturopathic case management is predicated upon the understanding of an intimately interconnected internal physiological and external context of the human organism-potentially indicating a worldview aligned with a complexity perspective. In this study we investigate naturopathic clinical reasoning using a complexity lens with the aim of ascertaining the extent of correspondence between the two.

METHOD

Mind maps depicting case presentations were sought from Australian degree qualified naturopaths. A network mapping was undertaken, which was then analysed in accordance with a complexity science framework using exploratory data analysis and network analysis processes and tools.

RESULTS

Naturopathic case schematics, in the form of mind maps (n = 70), were collected, network mapped, and analysed. A total of 739 unique elements and 2724 links were identified across the network. Integral elements across the network were: stress, fatigue, general anxiety, systemic inflammation, gut dysbiosis, and diet. A modularity algorithm detected 11 communities, the primary ones of these representing the nervous system and mood; the gastrointestinal tract, liver, and nutrition; immune function and the immune system; and diet and nutrients.

CONCLUSIONS

Naturopathic case management is holistic and based on a perspective of an integrated physiology and external context of the human organism. The traditional concept of holism, when subjected to a complexity lens, leads to the emergence of a contemporary holistic paradigm cognisant of the human organism being a complex system. The application of complexity science to investigate naturopathic case management as employed in this study, demonstrates that it is possible to investigate traditional philosophies and principles in a scientific and critical manner. A complexity science research approach may offer a suitable scientific paradigm to develop our understanding of traditional whole systems of medicine.

Primary health care case management through the lens of complexity: an exploratory study of naturopathic practice using complexity science principles

Background

Advances in systems science creates an opportunity to bring a complexity perspective to health care practices and research. While medical knowledge has greatly progressed using a reductionist and mechanistic philosophy, this approach may be limited in its capacity to manage chronic and complex illness. With its holistic foundation, naturopathy is a primary health profession with a purported alignment with a complexity perspective. As such this pilot study aimed to investigate the application of complexity science principles, strategies, and tools to primary health care using naturopathy as a case study.

Methods

A network mapping and analysis of the naturopathic case management process was conducted. Mind maps were created by naturopathic practitioners to reflect their clinical conceptualisation of a common paper clinical case. These mind maps were inputed into Gephi, a network mapping, exploration, and analysis software. Various layouts of the data were produced, and these were analysed using exploratory data analysis and computational network analysis.

Results

Seven naturopathic practitioners participated in the study. In the combined network mapping, 133 unique elements and 399 links were identified. Obesity, the presenting issue in the case, was centrally located. Along with obesity, other keystone elements included: systemic inflammation, dysbiosis, diet, the liver, and mood. Each element was connected on average to 3.05 other elements, with a degree variation between one and 36. Six communities within the dataset were identified, comprising: the nervous system and mood, gastroinstetinal and dietary factors, systemic inflammation and obesity, the endocrine system and metabolism.

Conclusions

This pilot study demonstrates that it is feasible to apply a complexity science perspective to investigating primary health care case management. This supports a shift to viewing the human organism as a complex adaptive system within primary health care settings, with implications for health care practices that are more cognisant with the treatment of chronic and complex conditions and research opportunities to capture the complex clinical reasoning processes of practitioners.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12906-022-03585-2.

Supporting Computational Apprenticeship Through Educational and Software Infrastructure: A Case Study in a Mathematical Oncology Research Lab

There is growing awareness of the need for mathematics and computing to quantitatively understand the complex dynamics and feedbacks in the life sciences. Although several institutions and research groups are conducting pioneering multidisciplinary research, communication and education across fields remain a bottleneck. The opportunity is ripe for using education research-supported mechanisms of cross-disciplinary training at the intersection of mathematics, computation, and biology. This case study uses the computational apprenticeship theoretical framework to describe the efforts of a computational biology lab to rapidly prototype, test, and refine a mentorship infrastructure for undergraduate research experiences. We describe the challenges, benefits, and lessons learned, as well as the utility of the computational apprenticeship framework in supporting computational/math students learning and contributing to biology, and biologists in learning computational methods. We also explore implications for undergraduate classroom instruction and cross-disciplinary scientific communication.

The Systems and Synthetic Biology of Auxin

Auxin biology as a field has been at the forefront of advances in delineating the structures, dynamics, and control of plant growth networks. Advances have been enabled by combining the complementary fields of top-down, holistic systems biology and bottom-up, build-to-understand synthetic biology. Continued collaboration between these approaches will facilitate our understanding of and ability to engineer auxin's control of plant growth, development, and physiology. There is a need for the application of similar complementary approaches to improving equity and justice through analysis and redesign of the human systems in which this research is undertaken.

Embracing the Complexity of Primary Health Care: System-Based Tools and Strategies for Researching the Case Management Process

The provision of health care is frequently a complex process, and favourable clinical outcomes are dependent on the effective management of this complexity. Contemporary medicine and health care practices that are biomedically aligned have been informed by a reductionist paradigm, potentially creating a misalignment between health care and the human organism as a complex adaptive system. Complexity science is increasingly gaining momentum within the academic literature and is being employed across a wide range of scientific disciplines, although this is less evident in medicine. Limited evidence was found within the literature of a complexity science framework being used to explore and inform individual health care practices; in this paper, this gap will be explored through consideration of the use of strategies and tools (specifically mind maps, computer-generated network mappings, exploratory data analysis, and computer-derived network analysis) which are congruent with a complexity science framework. This information may be useful to researchers investigating health care provision and to clinicians wishing to incorporate a complexity sensibility within their practice.

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Multiscale Computational Modeling of Vascular Adaptation: A Systems Biology Approach Using Agent-Based Models

The widespread incidence of cardiovascular diseases and associated mortality and morbidity, along with the advent of powerful computational resources, have fostered an extensive research in computational modeling of vascular pathophysiology field and promoted in-silico models as a support for biomedical research. Given the multiscale nature of biological systems, the integration of phenomena at different spatial and temporal scales has emerged to be essential in capturing mechanobiological mechanisms underlying vascular adaptation processes. In this regard, agent-based models have demonstrated to successfully embed the systems biology principles and capture the emergent behavior of cellular systems under different pathophysiological conditions. Furthermore, through their modular structure, agent-based models are suitable to be integrated with continuum-based models within a multiscale framework that can link the molecular pathways to the cell and tissue levels. This can allow improving existing therapies and/or developing new therapeutic strategies. The present review examines the multiscale computational frameworks of vascular adaptation with an emphasis on the integration of agent-based approaches with continuum models to describe vascular pathophysiology in a systems biology perspective. The state-of-the-art highlights the current gaps and limitations in the field, thus shedding light on new areas to be explored that may become the future research focus. The inclusion of molecular intracellular pathways (e.g., genomics or proteomics) within the multiscale agent-based modeling frameworks will certainly provide a great contribution to the promising personalized medicine. Efforts will be also needed to address the challenges encountered for the verification, uncertainty quantification, calibration and validation of these multiscale frameworks.

A Multilayer Interactome Network Constructed in a Forest Poplar Population Mediates the Pleiotropic Control of Complex Traits

The effects of genes on physiological and biochemical processes are interrelated and interdependent; it is common for genes to express pleiotropic control of complex traits. However, the study of gene expression and participating pathways in vivo at the whole-genome level is challenging. Here, we develop a coupled regulatory interaction differential equation to assess overall and independent genetic effects on trait growth. Based on evolutionary game theory and developmental modularity theory, we constructed multilayer, omnigenic networks of bidirectional, weighted, and positive or negative epistatic interactions using a forest poplar tree mapping population, which were organized into metagalactic, intergalactic, and local interstellar networks that describe layers of structure between modules, submodules, and individual single nucleotide polymorphisms, respectively. These multilayer interactomes enable the exploration of complex interactions between genes, and the analysis of not only differential expression of quantitative trait loci but also previously uncharacterized determinant SNPs, which are negatively regulated by other SNPs, based on the deconstruction of genetic effects to their component parts. Our research framework provides a tool to comprehend the pleiotropic control of complex traits and explores the inherent directional connections between genes in the structure of omnigenic networks.

Beyond the limit: carrying capacity (K) and the holism/reductionism debate

As the debate about holism and reductionism in ecology has ebbed in the last twenty years, this article aims to reassess the traditional opposition between holistic and reductionist epistemologies during the development of population biology. The history of the notion of carrying capacity, the upper demographic limit of a viable population, will be analyzed as a paradigmatic case of the progressive imposition of reductionist strategies, from both an epistemological and a semantic point of view, since the middle of the twentieth century. Then, Richard Looijen's reduction of the carrying capacity concept to the niche partitioning theory will be assessed and rebuked for both empirical and logical reasons. Eventually, some recent "weak" and "hard" emergent conceptualizations of the notion of carrying capacity, in logistic map models or in coupled niche-population systems, will be presented in order to show how they call into question the nature and the use of the notion of carrying capacity as a predefined ecological limit.

Molecules, Information and the Origin of Life: What Is Next?

How life did originate and what is life, in its deepest foundation? The texture of life is known to be held by molecules and their chemical-physical laws, yet a thorough elucidation of the aforementioned questions still stands as a puzzling challenge for science. Focusing solely on molecules and their laws has indirectly consolidated, in the scientific knowledge, a mechanistic (reductionist) perspective of biology and medicine. This occurred throughout the long historical path of experimental science, affecting subsequently the onset of the many theses and speculations about the origin of life and its maintenance. Actually, defining what is life, asks for a novel epistemology, a ground on which living systems’ organization, whose origin is still questioned via chemistry, physics and even philosophy, may provide a new key to focus onto the complex nature of the human being. In this scenario, many issues, such as the role of information and water structure, have been long time neglected from the theoretical basis on the origin of life and marginalized as a kind of scenic backstage. On the contrary, applied science and technology went ahead on considering molecules as the sole leading components in the scenery. Water physics and information dynamics may have a role in living systems much more fundamental than ever expected. Can an organism be simply explained by a mechanistic view of its nature or we need “something else”? Probably, we can earn sound foundations about life by simply changing our prejudicial view about living systems simply as complex, highly ordered machines. In this manuscript we would like to reappraise many fundamental aspects of molecular and chemical biology and reading them through a new paradigm, which includes Prigogine’s dissipative structures and informational dissipation (Shannon dissipation). This would provide readers with insightful clues about how biology and chemistry may be thoroughly revised, referring to new models, such as informational dissipation. We trust they are enabled to address a straightforward contribution in elucidating what life is for science. This overview is not simply a philosophical speculation, but it would like to affect deeply our way to conceive and describe the foundations of organisms’ life, providing intriguing suggestions for readers in the field.

Tools for time-course simulation in systems biology: a brief overview

Dynamic modeling of biological systems is essential for understanding all properties of a given organism as it allows us to look not only at the static picture of an organism but also at its behavior under various conditions. With the increasing amount of experimental data, the number of tools that enable dynamic analysis also grows. However, various tools are based on different approaches, use different types of data and offer different functions for analyses; so it can be difficult to choose the most suitable tool for a selected type of model. Here, we bring a brief overview containing descriptions of 50 tools for the reconstruction of biological models, their time-course simulation and dynamic analysis. We examined each tool using test data and divided them based on the qualitative and quantitative nature of the mathematical apparatus they use.

Multitissue Multiomics Systems Biology to Dissect Complex Diseases

Most complex diseases involve genetic and environmental risk factors, engage multiple cells and tissues, and follow a polygenic or omnigenic model depicting numerous genes contributing to pathophysiology. These multidimensional complexities pose challenges to traditional approaches that examine individual factors. In turn, multitissue multiomics systems biology has emerged to comprehensively elucidate within- and cross-tissue molecular networks underlying gene-by-environment interactions and contributing to complex diseases. The power of systems biology in retrieving novel insights and formulating new hypotheses has been well documented. However, the field faces various challenges that call for debate and action. In this opinion article, I discuss the concepts, benefits, current state, and challenges of the field and point to the next steps toward network-based systems medicine.

Cognitive science as complexity science

It is uncontroversial to claim that cognitive science studies many complex phenomena. What is less acknowledged are the contradictions among many traditional commitments of its investigative approaches and the nature of cognitive systems. Consider, for example, methodological tensions that arise due to the fact that like most natural systems, cognitive systems are nonlinear; and yet, traditionally cognitive science has relied on linear statistical data analyses. Cognitive science as complexity science is offered as an interdisciplinary framework for the investigation of cognition that can dissolve such contradictions and tensions. Here, cognition is treated as exhibiting the following four key features: emergence, nonlinearity, self-organization, and universality. This framework integrates concepts, methods, and theories from such disciplines as systems theory, nonlinear dynamical systems theory, and synergetics. By adopting this approach, the cognitive sciences benefit from a common set of practices to investigate, explain, and understand cognition in its varied and complex forms. This article is categorized under: Computer Science > Neural Networks Psychology > Theory and Methods Philosophy > Foundations of Cognitive Science Neuroscience > Cognition.

The superior longitudinal fasciculus and its functional triple-network mechanisms in brooding

Brooding, which refers to a repetitive focus on one's distress, is associated with functional connectivity within Default-Mode, Salience, and Executive-Control networks (DMN; SN; ECN), comprising the so-called "triple-network" of attention. Individual differences in brain structure that might perseverate dysfunctional connectivity of brain networks associated with brooding are less clear, however. Using diffusion and functional Magnetic Resonance Imaging, we explored multimodal relationships between brooding severity, white-matter microstructure, and resting-state functional connectivity in depressed adults (N = 32-44), and then examined whether findings directly replicated in a demographically-similar, independent sample (N = 36-45). Among the fully-replicated results, three core findings emerged. First, brooding severity is associated with functional integration and segregation of the triple-network, particularly with a Precuneal subnetwork of the DMN. Second, microstructural asymmetry of the Superior Longitudinal Fasciculus (SLF) provides a robust structural connectivity basis for brooding and may account for over 20% of its severity (Discovery: adj. R² = 0.18; Replication: adj. R² = 0.22; MSE = 0.06, Predictive R² = 0.22). Finally, microstructure of the right SLF and auxiliary white-matter is associated with the functional connectivity correlates of brooding, both within and between components of the triple-network (Discovery: adj. R² = 0.21; Replication: adj. R² = 0.18; MSE = 0.03, Predictive R² = 0.21-0.22). By cross-validating multimodal discovery with replication, the present findings help to reproducibly unify disparate perspectives of brooding etiology. Based on that synthesis, our study reformulates brooding as a microstructural-functional connectivity neurophenotype.

Vocalization Subsystem Responses to a Temporarily Induced Unilateral Vocal Fold Paralysis

PURPOSE

The purpose of this study is to quantify the interactions of the 3 vocalization subsystems of respiration, phonation, and resonance before, during, and after a perturbation to the larynx (temporarily induced unilateral vocal fold paralysis) in 10 vocally healthy participants. Using dynamic systems theory as a guide, we hypothesized that data groupings would emerge revealing context-dependent patterns in the relationships of variables representing the 3 vocalization subsystems. We also hypothesized that group data would mask important individual variability important to understanding the relationships among the vocalization subsystems.

METHOD

A perturbation paradigm was used to obtain respiratory kinematic, aerodynamic, and acoustic formant measures from 10 healthy participants (8 women, 2 men) with normal voices. Group and individual data were analyzed to provide a multilevel analysis of the data. A 3-dimensional state space model was constructed to demonstrate the interactive relationships among the 3 subsystems before, during, and after perturbation.

RESULTS

During perturbation, group data revealed that lung volume initiations and terminations were lower, with longer respiratory excursions; airflow rates increased while subglottic pressures were maintained. Acoustic formant measures indicated that the spacing between the upper formants decreased (F3-F5), whereas the spacing between F1 and F2 increased. State space modeling revealed the changing directionality and interactions among the 3 subsystems.

CONCLUSIONS

Group data alone masked important variability necessary to understand the unique relationships among the 3 subsystems. Multilevel analysis permitted a richer understanding of the individual differences in phonatory regulation and permitted subgroup analysis. Dynamic systems theory may be a useful heuristic to model the interactive relationships among vocalization subsystems.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.5913532.

Development of a Preventive HIV Vaccine Requires Solving Inverse Problems Which Is Unattainable by Rational Vaccine Design

Hypotheses and theories are essential constituents of the scientific method. Many vaccinologists are unaware that the problems they try to solve are mostly inverse problems that consist in imagining what could bring about a desired outcome. An inverse problem starts with the result and tries to guess what are the multiple causes that could have produced it. Compared to the usual direct scientific problems that start with the causes and derive or calculate the results using deductive reasoning and known mechanisms, solving an inverse problem uses a less reliable inductive approach and requires the development of a theoretical model that may have different solutions or none at all. Unsuccessful attempts to solve inverse problems in HIV vaccinology by reductionist methods, systems biology and structure-based reverse vaccinology are described. The popular strategy known as rational vaccine design is unable to solve the multiple inverse problems faced by HIV vaccine developers. The term “rational” is derived from “rational drug design” which uses the 3D structure of a biological target for designing molecules that will selectively bind to it and inhibit its biological activity. In vaccine design, however, the word “rational” simply means that the investigator is concentrating on parts of the system for which molecular information is available. The economist and Nobel laureate Herbert Simon introduced the concept of “bounded rationality” to explain why the complexity of the world economic system makes it impossible, for instance, to predict an event like the financial crash of 2007–2008. Humans always operate under unavoidable constraints such as insufficient information, a limited capacity to process huge amounts of data and a limited amount of time available to reach a decision. Such limitations always prevent us from achieving the complete understanding and optimization of a complex system that would be needed to achieve a truly rational design process. This is why the complexity of the human immune system prevents us from rationally designing an HIV vaccine by solving inverse problems.

Lessons from culturally contrasted alternative methods of inquiry and styles of comprehension for the new foundations in the study of life

Contemporary scientific approaches to Biology are the result of some cultural ideas considered as universal by Western reductionist traditions. The study of the cultural, symbolic and historical approaches to reality and Life provides us important lessons about the necessity of integrating Eastern holistic views into the study of Life. This is both an epistemological and ontological enhancement which provides more powerful and insightful ways to deal with Life and its understanding.

Nature and Consequences of Biological Reductionism for the Immunological Study of Infectious Diseases

Evolution has conserved "economic" systems that perform many functions, faster or better, with less. For example, three to five leukocyte types protect from thousands of pathogens. To achieve so much with so little, biological systems combine their limited elements, creating complex structures. Yet, the prevalent research paradigm is reductionist. Focusing on infectious diseases, reductionist and non-reductionist views are here described. The literature indicates that reductionism is associated with information loss and errors, while non-reductionist operations can extract more information from the same data. When designed to capture one-to-many/many-to-one interactions-including the use of arrows that connect pairs of consecutive observations-non-reductionist (spatial-temporal) constructs eliminate data variability from all dimensions, except along one line, while arrows describe the directionality of temporal changes that occur along the line. To validate the patterns detected by non-reductionist operations, reductionist procedures are needed. Integrated (non-reductionist and reductionist) methods can (i) distinguish data subsets that differ immunologically and statistically; (ii) differentiate false-negative from -positive errors; (iii) discriminate disease stages; (iv) capture in vivo, multilevel interactions that consider the patient, the microbe, and antibiotic-mediated responses; and (v) assess dynamics. Integrated methods provide repeatable and biologically interpretable information.

Applying a systems approach to thyroid physiology: Looking at the whole with a mitochondrial perspective instead of judging single TSH values or why we should know more about mitochondria to understand metabolism

Classical thinking in endocrine physiology squeezes our diagnostic handling into a simple negative feedback mechanism with a controller and a controlled variable. In the case of the thyroid this is reduced to TSH and fT3 and fT4, respectively. The setting of this tight notion has no free space for any additions. In this paper we want to challenge this model of limited application by proposing a construct based on a systems approach departing from two basic considerations. In first place since the majority of cases of thyroid disease develop and appear during life it has to be considered as an acquired condition. In the second place, our experience with the reversibility of morphological changes makes the autoimmune theory inconsistent.

While medical complexity can expand into the era of OMICS as well as into one where manipulations with the use of knock-outs and -ins are common in science, we have preferred to maintain a simple and practical approach. We will describe the interactions of iron, magnesium, zinc, selenium and coenzyme Q10 with the thyroid axis. The discourse will be then brought into the context of ovarian function, i.e. steroid hormone production. Finally the same elemental players will be presented in relation to the basic mitochondrial machinery that supports the endocrine.

We propose that an intact mitochondrial function can guard the normal endocrine function of both the thyroid as well as of the ovarian axis. The basic elements required for this function appear to be magnesium and iron. In the case of the thyroid, magnesium-ATP acts in iodine uptake and the heme protein peroxidase in thyroid hormone synthesis. A similar biochemical process is found in steroid synthesis with cholesterol uptake being the initial energy-dependent step and later the heme protein ferredoxin 1 which is required for steroid synthesis. Magnesium plays a central role in determining the clinical picture associated with thyroid disease and is also involved in maintaining fertility. With the aid of 3D sonography patients needing selenium and/or coenzyme Q10 can be easily identified. By this we firmly believe that physicians should know more about basic biochemistry and the way it fits into mitochondrial function in order to understand metabolism. Contemplating only TSH is highly reductionistic.

Outline

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Author's profiles and motivation for this analysis

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The philosophical alternatives in science and medicine

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Reductionism vs. systems approach in clinical thyroid disease guidelines

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The entry into complexity: the involvement of the musculoskeletal system

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Integrating East and West: teachings from Chinese Medicine and from evidence based medicine (EBM)

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Can a mathematical model represent complexity in the daily thyroid practice?

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How effective is thyroxine treatment?

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Resolving the situation of residual symptoms in treated patients with thyroid disease

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Importance of iron, zinc and magnesium in relation to thyroid function

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Putting together new concepts related to thyroid function for a systems approach

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Expanding our model into general aspects of medicine

Multidimensional Complexity of Cancer. Simple Solutions Are Needed

Cancer is a complex system. Tumor complexity is determined not only by genetic and epigenetic heterogeneity, but also by a huge number of interactions between cancer and normal cells. The heterogeneity and complexity of a tumor causes failure of molecular targeting therapy as a tool for fighting cancer. This review considers the concepts of malignant tumors as organisms that have common characteristics despite all heterogeneity. This leads to the idea that one of the most promising strategies for fighting cancer is the use of the patient's immune system.

Stability and its manifestation in the chemical and biological worlds

Bridging between the phenomenologically distinct biological and physical worlds has been a major scientific challenge since Boltzmann's probabilistic formulation of the second law of thermodynamics. In this review we summarize our recent theoretical attempts to bridge that divide through analysis of the thermodynamic-kinetic interplay in chemical processes and the manner in which that interplay impacts on material stability. Key findings are that the term 'stability' manifests two facets - time and energy - and that stability's time facet, expressed as persistence, is more general than its energy facet. That idea, together with the proposed existence of a logical law of nature, the persistence principle, leads to the mathematically-based insight that stability can come about through either Boltzmann's probabilistic considerations or Malthusian kinetics. Two mathematically-based forms of material persistence then lead directly to the physical likelihood of two material forms, animate and inanimate. Significantly, the incorporation of kinetic considerations into the stability concept appears to bring us closer to enabling two of the central theories in science - the second law of thermodynamics and Darwin's theory of evolution - to be reconciled within a single conceptual framework.

Neuropharmacology beyond reductionism - A likely prospect

Neuropharmacology had several major past successes, but the last few decades did not witness any leap forward in the drug treatment of brain disorders. Moreover, current drugs used in neurology and psychiatry alleviate the symptoms, while hardly curing any cause of disease, basically because the etiology of most neuro-psychic syndromes is but poorly known. This review argues that this largely derives from the unbalanced prevalence in neuroscience of the analytic reductionist approach, focused on the cellular and molecular level, while the understanding of integrated brain activities remains flimsier. The decline of drug discovery output in the last decades, quite obvious in neuropharmacology, coincided with the advent of the single target-focused search of potent ligands selective for a well-defined protein, deemed critical in a given pathology. However, all the widespread neuro-psychic troubles are multi-mechanistic and polygenic, their complex etiology making unsuited the single-target drug discovery. An evolving approach, based on systems biology considers that a disease expresses a disturbance of the network of interactions underlying organismic functions, rather than alteration of single molecular components. Accordingly, systems pharmacology seeks to restore a disturbed network via multi-targeted drugs. This review notices that neuropharmacology in fact relies on drugs which are multi-target, this feature having occurred just because those drugs were selected by phenotypic screening in vivo, or emerged from serendipitous clinical observations. The novel systems pharmacology aims, however, to devise ab initio multi-target drugs that will appropriately act on multiple molecular entities. Though this is a task much more complex than the single-target strategy, major informatics resources and computational tools for the systemic approach of drug discovery are already set forth and their rapid progress forecasts promising outcomes for neuropharmacology.

The multiple and complex and changeable scenarios of the Trypanosoma cruzi transmission cycle in the sylvatic environment

In this study, we report and discuss the results generated from over 20 years of studies of the Trypanosoma cruzi sylvatic transmission cycle. Our results have uncovered new aspects and reviewed old concepts on issues including reservoirs, true generalist species, association of mammalian species with distinct discrete typing units - DTUs, distribution of T. cruzi genotypes in the wild, mixed infections, and T. cruzi transmission ecology. Using parasitological and serological tests, we examined T. cruzi infection in 7,285 mammalian specimens from nine mammalian orders dispersed all over the Brazilian biomes. The obtained T. cruzi isolates were characterized by mini-exon gene sequence polymorphism and PCR RFLP to identify DTUs. Infection by T. cruzi was detected by serological methods in 20% of the examined animals and isolated from 41% of those infected, corresponding to 8% of all the examined mammals. Each mammal taxon responded uniquely to T. cruzi infection. Didelphis spp. are able to maintain high and long-lasting parasitemias (positive hemocultures) caused by TcI but maintain and rapidly control parasitemias caused by TcII to almost undetectable levels. In contrast, the tamarin species Leontopithecus rosalia and L. chrysomelas maintain long-lasting and high parasitemias caused by TcII similarly to Philander sp. The coati Nasua nasua maintains high parasitemias by both parental T. cruzi DTUs TcI or TcII and by TcII/TcIV (formerly Z3) at detectable levels. Wild and domestic canidae seem to display only a short period of reservoir competence. T. cruzi infection was demonstrated in the wild canid species Cerdocyon thous and Chrysocyon brachyurus, and positive hemoculture was obtained in one hyper carnivore species (Leopardus pardalis), demonstrating that T. cruzi transmission is deeply immersed in the trophic net. T. cruzi DTU distribution in nature did not exhibit any association with a particular biome or habitat. TcI predominates throughout (58% of the T. cruzi isolates); however, in spite of being significantly less frequent (17%), TcII is also widely distributed. Concomitant DTU infection occurred in 16% of infected mammals of all biomes and included arboreal and terrestrial species, as well as bats. TcI/TcII concomitant infection was the most common and widely dispersed, with mixed TcI/TcII infections especially common in coatis and in Didelphimorphia. The second most common pattern of concomitant infection was TcI/TcIV, observed in Chiroptera, Didelphimorphia and Primates. Taken together, our results demonstrate the complexity of T. cruzi reservoir system and its transmission strategies, indicating that there is considerably more to be learned regarding ecology of T. cruzi.

Systems biology approaches to enhance our understanding of drug hypersensitivity reactions

Hypersensitivity drug reactions (HDRs) encompass a wide spectrum of unpredictable clinical entities. They represent an important health problem, affecting people of all ages, and lead to a large strain on the public health system. Here, we summarize experiments that use high-throughput genomics technologies to investigate HDRs. We also introduce the field of systems biology as a relatively recent discipline concerned with the integration and analysis of high-throughput data sets such as DNA microarrays and next-generation sequencing data. We describe previous studies that have applied systems biology techniques to related fields such as allergy and asthma. Finally, we present a number of potential applications of systems biology to the study of HDRs, in order to make the reader aware of the types of analyses that can be performed and the insights that can be gained through their application.

The Saccharomyces cerevisiae transcriptome as a mirror of phytochemical variation in complex extracts of Equisetum arvense from America, China, Europe and India

BACKGROUND

Pattern-oriented chemical profiling is increasingly being used to characterize the phytochemical composition of herbal medicines for quality control purposes. Ideally, a fingerprint of the biological effects should complement the chemical fingerprint. For ethical and practical reasons it is not possible to test each herbal extract in laboratory animals or humans. What is needed is a test system consisting of an organism with relevant biology and complexity that can serve as a surrogate in vitro system. The purpose of this study was to test the hypothesis that the Saccharomyces cerevisiae transcriptome might be used as an indicator of phytochemical variation of closely-related yet distinctly different extracts prepared from a single species of a phytogeographically widely distributed medicinal plant. We combined phytochemical profiling using chromatographic methods (HPTLC, HPLC-PDA-MS/MS) and gene expression studies using Affymetrix Yeast 2.0 gene chip with principal component analysis and k-nearest neighbor clustering analysis to test this hypothesis using extracts prepared from the phytogeographically widely distributed medicinal plant Equisetum arvense as a test case.

RESULTS

We found that the Equisetum arvense extracts exhibited qualitative and quantitative differences in their phytochemical composition grouped along their phytogeographical origin. Exposure of yeast to the extracts led to changes in gene expression that reflected both the similarities and differences in the phytochemical composition of the extracts. The Equisetum arvense extracts elicited changes in the expression of genes involved in mRNA translation, drug transport, metabolism of energy reserves, phospholipid metabolism, and the cellular stress response.

CONCLUSIONS

Our data show that functional genomics in S. cerevisiae may be developed as a sensitive bioassay for the scientific investigation of the interplay between phytochemical composition and transcriptional effects of complex mixtures of chemical compounds. S. cerevisiae transcriptomics may also be developed for testing of mixtures of conventional drugs ("polypills") to discover novel antagonistic or synergistic effects of those drug combinations.

Systematic reviews of animal models: methodology versus epistemology

Systematic reviews are currently favored methods of evaluating research in order to reach conclusions regarding medical practice. The need for such reviews is necessitated by the fact that no research is perfect and experts are prone to bias. By combining many studies that fulfill specific criteria, one hopes that the strengths can be multiplied and thus reliable conclusions attained. Potential flaws in this process include the assumptions that underlie the research under examination. If the assumptions, or axioms, upon which the research studies are based, are untenable either scientifically or logically, then the results must be highly suspect regardless of the otherwise high quality of the studies or the systematic reviews. We outline recent criticisms of animal-based research, namely that animal models are failing to predict human responses. It is this failure that is purportedly being corrected via systematic reviews. We then examine the assumption that animal models can predict human outcomes to perturbations such as disease or drugs, even under the best of circumstances. We examine the use of animal models in light of empirical evidence comparing human outcomes to those from animal models, complexity theory, and evolutionary biology. We conclude that even if legitimate criticisms of animal models were addressed, through standardization of protocols and systematic reviews, the animal model would still fail as a predictive modality for human response to drugs and disease. Therefore, systematic reviews and meta-analyses of animal-based research are poor tools for attempting to reach conclusions regarding human interventions.

Promise of personalized omics to precision medicine

The rapid development of high-throughput technologies and computational frameworks enables the examination of biological systems in unprecedented detail. The ability to study biological phenomena at omics levels in turn is expected to lead to significant advances in personalized and precision medicine. Patients can be treated according to their own molecular characteristics. Individual omes as well as the integrated profiles of multiple omes, such as the genome, the epigenome, the transcriptome, the proteome, the metabolome, the antibodyome, and other omics information are expected to be valuable for health monitoring, preventative measures, and precision medicine. Moreover, omics technologies have the potential to transform medicine from traditional symptom-oriented diagnosis and treatment of diseases toward disease prevention and early diagnostics. We discuss here the advances and challenges in systems biology-powered personalized medicine at its current stage, as well as a prospective view of future personalized health care at the end of this review.