How to help students understand physiology? Emphasize general models

Students generally approach topics in physiology as a series of unrelated phenomena that share few underlying principles. In many students' view, the Fick equation for cardiac output is fundamentally different from a renal clearance equation. If, however, students recognize that these apparently different situations can be viewed as examples of the same general conceptual model (e.g., conservation of mass), they may gain a more unified understanding of physiological systems. An understanding of as few as seven general models can provide students with an initial conceptual framework for analyzing most physiological systems. The general models deal with control systems, conservation of mass, mass and heat flow, elastic properties of tissues, transport across membranes, cell-to-cell communication, and molecular interaction.     

Exploring causal networks of bovine milk fatty acids in a multivariate mixed model context

Background

Knowledge regarding causal relationships among traits is important to understand complex biological systems. Structural equation models (SEM) can be used to quantify the causal relations between traits, which allow prediction of outcomes to interventions applied to such a network. Such models are fitted conditionally on a causal structure among traits, represented by a directed acyclic graph and an Inductive Causation (IC) algorithm can be used to search for causal structures. The aim of this study was to explore the space of causal structures involving bovine milk fatty acids and to select a network supported by data as the structure of a SEM.

Results

The IC algorithm adapted to mixed models settings was applied to study 14 correlated bovine milk fatty acids, resulting in an undirected network. The undirected pathway from C4:0 to C12:0 resembled the de novo synthesis pathway of short and medium chain saturated fatty acids. By using prior knowledge, directions were assigned to that part of the network and the resulting structure was used to fit a SEM that led to structural coefficients ranging from 0.85 to 1.05. The deviance information criterion indicated that the SEM was more plausible than the multi-trait model.

Conclusions

The IC algorithm output pointed towards causal relations between the studied traits. This changed the focus from marginal associations between traits to direct relationships, thus towards relationships that may result in changes when external interventions are applied. The causal structure can give more insight into underlying mechanisms and the SEM can predict conditional changes due to such interventions.     

Organic tissues in rotating bioreactors: fluid-mechanical aspects, dynamic growth models, and morphological evolution

This analysis deals with advances in tissue-engineering models and computational methods as well as with novel results on the relative importance of "controlling forces" in the growth of organic constructs. Specifically, attention is focused on the rotary culture system, because this technique has proven to be the most practical solution for providing a suitable culture environment supporting three-dimensional tissue assemblies. From a numerical point of view, the growing biological specimen gives rise to a moving boundary problem. A "volume-of-fraction" method is specifically and carefully developed according to the complex properties and mechanisms of organic tissue growth and, in particular, taking into account the sensitivity of the construct/liquid interface to the effect of the fluid-dynamic shear stress (it induces changes in tissue metabolism and function that elicit a physiological response from the biological cells). The present study uses available data to introduce a set of growth models. The surface conditions are coupled to the transfer of mass and momentum at the specimen/culture-medium interface and lead to the introduction of a group of differential equations for the nutrient concentration around the sample and for the evolution of tissue mass displacement. The models are then used to show how the proposed surface kinetic laws can predict (through sophisticated numerical simulations) many of the known characteristics of biological tissues grown using rotating-wall perfused vessel bioreactors. This procedure provides a validation of the models and associated numerical method and also gives insight into the mechanisms of the phenomena. The interplay between the increasing size of the tissue and the structure of the convective field is investigated. It is shown that this interaction is essential in determining the time evolution of the tissue shape. The size of the growing specimen plays a critical role with regard to the intensity of convection and the related shear stresses. Convective effects, in turn, are found to impact growth rates, tissue size, and morphology, as well as the mechanisms driving growth. The method exhibits novel capabilities to predict and elucidate experimental observations and to identify cause-and-effect relationships.     

Use of concept mapping in an undergraduate introductory exercise physiology course

Physiology is often considered a challenging course for students. It is up to teachers to structure courses and create learning opportunities that will increase the chance of student success. In an undergraduate exercise physiology course, concept maps are assigned to help students actively process and organize information into manageable and meaningful chunks and to teach them to recognize the patterns and regularities of physiology. Students are first introduced to concept mapping with a commonly relatable nonphysiology concept and are then assigned a series of maps that become more and more complex. Students map the acute response to a drop in blood pressure, the causes of the acute increase in stroke volume during cardiorespiratory exercise, and the factors contributing to an increase in maximal O(2) consumption with cardiorespiratory endurance training. In the process, students draw the integrative nature of physiology, identify causal relationships, and learn about general models and core principles of physiology.     

Regression-based multi-trait QTL mapping using a structural equation model

Quantitative trait loci (QTL) mapping often results in data on a number of traits that have well-established causal relationships. Many multi-trait QTL mapping methods that account for the correlation among multiple traits have been developed to improve the statistical power and the precision of QTL parameter estimation. However, none of these methods are capable of incorporating the causal structure among the traits. Consequently, genetic functions of the QTL may not be fully understood. Structural equation modeling (SEM) allows researchers to explicitly characterize the causal structure among the variables and to decompose effects into direct, indirect, and total effects. In this paper, we developed a multi-trait SEM method of QTL mapping that takes into account the causal relationships among traits related to grain yield. Performance of the proposed method is evaluated by simulation study and applied to data from a wheat experiment. Compared with single trait analysis and the multi-trait least-squares analysis, our multi-trait SEM improves statistical power of QTL detection and provides important insight into how QTLs regulate traits by investigating the direct, indirect, and total QTL effects. The approach also helps build biological models that more realistically reflect the complex relationships among QTL and traits and is more precise and efficient in QTL mapping than single trait analysis.     

Genetic Causation in Complex Regulatory Systems: An Integrative Dynamic Perspective

The logic of genetic discovery has changed little over time, but the focus of biology is shifting from simple genotype–phenotype relationships to complex metabolic, physiological, developmental, and behavioral traits. In light of this, the traditional reductionist view of individual genes as privileged difference-making causes of phenotypes is re-examined. The scope and nature of genetic effects in complex regulatory systems, in which dynamics are driven by regulatory feedback and hierarchical interactions across levels of organization are considered. This review argues that it is appropriate to treat genes as specific actual difference-makers for the molecular regulation of gene expression. However, they are often neither stable, proportional, nor specific as causes of the overall dynamic behavior of regulatory networks. Dynamical models, properly formulated and validated, provide the tools to probe cause-and-effect relationships in complex biological systems, allowing to go beyond the limitations of genetic reductionism to gain an integrative understanding of the causal processes underlying complex phenotypes.     

Cytogerontology at the beginning of the third millenium: from "correlative" to "gist" models

For the most part, research in the area of cytogerontology, i.e., investigation of the mechanisms of aging in the experiments on cultured cells, is carried out using the "Hayflick's model". More than forty years have passed since the appearance of that model, and during this period of time, very much data were obtained on its basis. These data contributed significantly to our knowledge of the behavior of both animal and human cultured cells. Specifically, we already know of the mechanisms underlying the aging in vitro. On the other hand, in my opinion, little has changed in our knowledge of the aging of the whole organism. In all likelihood, this can be explained by that the Hayflick's model is, like many others used in the experimental gerontology, correlative, i.e. based on a number of detected correlations. In the case of Hayflick's model, these are correlations between the mitotic potential of cells (cell population doubling potential) and some "gerontological" parameters and indices: species life-span, donor age, evidence of progeroid syndromes, etc., as well as various changes of normal (diploid) cells during long-term cultivation and during aging of the organism. It is, however, well known that very frequently a good correlation has nothing to do with the essence (gist) of the phenomenon. For example, we do know that the amount of gray hair correlates quite well with the age of an individual but is in no way related to the mechanisms of his/her aging and probability of death. In this case, the absence of cause-effect relationships is evident, which are, at the same time, indispensable for the development of gist models. These models, as distinct from the correlative ones, are based on a certain concept of aging. In the case of Hayflick's model, such a concept is absent: we cannot explain, using the "Hayflick's limit", why our organism ages. This conclusion was convincingly confirmed by the discovery of telomere mechanism which determines the aging of cells in vitro. That discovery initiated the appearance of theories attempting to explain the process of aging in vivo also on its basis. However, it has become clear that the mechanisms of aging of the entire organism, located, apparently, in its postmitotic cells, such as neurons or cardiomyocytes, cannot be explained in the framework of this approach. Hence, we believe that it is essential to develop "gist" models of aging using cultured cells. The mechanisms of cell aging in such models should be similar to the mechanisms of cell aging in the entire organism. Our "stationary phase aging" model could be one of such models, which is based on the assumption of the leading role of cell proliferation restriction in the processes of aging. We assume that the accumulation of "senile" damage is caused by the restriction of cell proliferation either due to the formation of differentiated cell populations during development (in vivo) or to the existence of saturation density phenomenon (in vitro). Cell proliferation changes themselves do not induce aging, they only lead to the accumulation of macromolecular defects, which, in turn, lead to the deterioration of tissues, organs, and, eventually, of the entire organism, increasing the probability of its death. Within the framework of our model, we define cell aging as the accumulation in a cell population of various types of damage identical to the damage arising in senescing multicellular organism. And, finally, it is essential to determine how the cell is dying and what the death of the cell is. These definitions will help to draw real parallels between the "genuine" aging of cells (i.e., increasing probability of their death with "age") and the aging of multicellular organisms.     

The "Pardridge" hypotheses relating to the role of hormone binding proteins in hormone delivery: a critique

Though the physiological role of specific binding proteins in serum is unknown, and the validity of the "free hormone hypothesis" has been challenged by several authors (including ourselves), we regard Pardridge's widely publicised objections to this hypothesis (and his recent suggestion of the existence of complex biochemical mechanisms causing the release of individual hormones in particular tissues) as deriving from an incorrect analysis of the effects of serum binding proteins on the kinetics of hormone transport, and therefore without foundation. This presentation is intended to substantiate this view.     

Estimating linear causality in the presence of latent variables

Learning causality from data is known as the causal discovery problem, and it is an important and relatively new field. In many applications, there often exist latent variables, if such latent variables are completely ignored, which can lead to the estimation results seriously biased. In this paper, a method of combining exploratory factor analysis and path analysis (EFA-PA) is proposed to infer the causality in the presence of latent variables. Our method expands latent variables as well as their linear causal relationships with observed variables, which enhances the accuracy of causal models. Such model can be thought of as the simplest possible causal models for continuous data. The EFA-PA is very similar to that of structural equation model, but the theoretical model established by the structural equation model needs to be modified in the process of data fitting until the ideal model is established.The model gained by EFA-PA not only avoids subjectivity but also reduces estimation complexity. It is found that the EFA-PA estimation model is superior to the other models. EFA-PA can provides a basis for the correct estimation of the causal relationship between the observed variables in the presence of latent variables. The experiment shows that EFA-PA is better than the structural equation model.     

Causal measures of structure and plasticity in simulated and living neural networks

A major goal of neuroscience is to understand the relationship between neural structures and their function. Recording of neural activity with arrays of electrodes is a primary tool employed toward this goal. However, the relationships among the neural activity recorded by these arrays are often highly complex making it problematic to accurately quantify a network's structural information and then relate that structure to its function. Current statistical methods including cross correlation and coherence have achieved only modest success in characterizing the structural connectivity. Over the last decade an alternative technique known as Granger causality is emerging within neuroscience. This technique, borrowed from the field of economics, provides a strong mathematical foundation based on linear auto-regression to detect and quantify "causal" relationships among different time series. This paper presents a combination of three Granger based analytical methods that can quickly provide a relatively complete representation of the causal structure within a neural network. These are a simple pairwise Granger causality metric, a conditional metric, and a little known computationally inexpensive subtractive conditional method. Each causal metric is first described and evaluated in a series of biologically plausible neural simulations. We then demonstrate how Granger causality can detect and quantify changes in the strength of those relationships during plasticity using 60 channel spike train data from an in vitro cortical network measured on a microelectrode array. We show that these metrics can not only detect the presence of causal relationships, they also provide crucial information about the strength and direction of that relationship, particularly when that relationship maybe changing during plasticity. Although we focus on the analysis of multichannel spike train data the metrics we describe are applicable to any stationary time series in which causal relationships among multiple measures is desired. These techniques can be especially useful when the interactions among those measures are highly complex, difficult to untangle, and maybe changing over time.     

High-Altitude Adaptation: Mechanistic Insights from Integrated Genomics and Physiology

Population genomic analyses of high-altitude humans and other vertebrates have identified numerous candidate genes for hypoxia adaptation, and the physiological pathways implicated by such analyses suggest testable hypotheses about underlying mechanisms. Studies of highland natives that integrate genomic data with experimental measures of physiological performance capacities and subordinate traits are revealing associations between genotypes (e.g., hypoxia-inducible factor gene variants) and hypoxia-responsive phenotypes. The subsequent search for causal mechanisms is complicated by the fact that observed genotypic associations with hypoxia-induced phenotypes may reflect second-order consequences of selection-mediated changes in other (unmeasured) traits that are coupled with the focal trait via feedback regulation. Manipulative experiments to decipher circuits of feedback control and patterns of phenotypic integration can help identify causal relationships that underlie observed genotype–phenotype associations. Such experiments are critical for correct inferences about phenotypic targets of selection and mechanisms of adaptation.     

The Teacher,the Physician and the Person: Exploring Causal Connections between Teaching Performance and Role Model Types Using Directed Acyclic Graphs

Background

In fledgling areas of research, evidence supporting causal assumptions is often scarce due to the small number of empirical studies conducted. In many studies it remains unclear what impact explicit and implicit causal assumptions have on the research findings; only the primary assumptions of the researchers are often presented. This is particularly true for research on the effect of faculty’s teaching performance on their role modeling. Therefore, there is a need for robust frameworks and methods for transparent formal presentation of the underlying causal assumptions used in assessing the causal effects of teaching performance on role modeling. This study explores the effects of different (plausible) causal assumptions on research outcomes.

Methods

This study revisits a previously published study about the influence of faculty’s teaching performance on their role modeling (as teacher-supervisor, physician and person). We drew eight directed acyclic graphs (DAGs) to visually represent different plausible causal relationships between the variables under study. These DAGs were subsequently translated into corresponding statistical models, and regression analyses were performed to estimate the associations between teaching performance and role modeling.

Results

The different causal models were compatible with major differences in the magnitude of the relationship between faculty’s teaching performance and their role modeling. Odds ratios for the associations between teaching performance and the three role model types ranged from 31.1 to 73.6 for the teacher-supervisor role, from 3.7 to 15.5 for the physician role, and from 2.8 to 13.8 for the person role.

Conclusions

Different sets of assumptions about causal relationships in role modeling research can be visually depicted using DAGs, which are then used to guide both statistical analysis and interpretation of results. Since study conclusions can be sensitive to different causal assumptions, results should be interpreted in the light of causal assumptions made in each study.     

Species-energy relationships at the macroecological scale: a review of the mechanisms

Correlations between the amount of energy received by an assemblage and the number of species that it contains are very general, and at the macro-scale such species-energy relationships typically follow a monotonically increasing curve. Whilst the ecological literature contains frequent reports of such relationships, debate on their causal mechanisms is limited and typically focuses on the role of energy availability in controlling the number of individuals in an assemblage. Assemblages from high-energy areas may contain more individuals enabling species to maintain larger, more viable populations, whose lower extinction risk elevates species richness. Other mechanisms have, however, also been suggested. Here we identify and clarify nine principal mechanisms that may generate positive species-energy relationships at the macro-scale. We critically assess their assumptions and applicability over a range of spatial scales, derive predictions for each and assess the evidence that supports or refutes them. Our synthesis demonstrates that all mechanisms share at least one of their predictions with an alternative mechanism. Some previous studies of species-energy relationships appear not to have recognised the extent of shared predictions, and this may detract from their contribution to the debate on causal mechanisms. The combination of predictions and assumptions made by each mechanism is, however, unique, suggesting that, in principle, conclusive tests are possible. Sufficient testing of all mechanisms has yet to be conducted, and no single mechanism currently has unequivocal support. Each may contribute to species-energy relationships in some circumstances, but some mechanisms are unlikely to act simultaneously. Moreover, a limited number appear particularly likely to contribute frequently to species-energy relationships at the macro-scale. The increased population size, niche position and diversification rate mechanisms are particularly noteworthy in this context.     

Hierarchies and causal relationships in interpretative models of the neoplastic process

The aim of this paper is to present a critical analysis of the kind of biological systems identified in the main explanatory theories of cancer (i.e. Somatic Mutation Theory and Tissue Organization Field Theory) and how references to the hierarchical organization of these biological systems are used in their explanatory arguments. I will discuss these aspects in terms of the isolation of the "locus of control" (Bechtel and Richardson 2010); that is, the point at which decisions are made shaping the explanatory endeavour. In fact, the current view of the neoplastic process, not as a static circumstance but as an evolving molecular and cellular process, makes it evident that the choice of the right level of analysis is not self-evident. This focus clarifies some epistemological reasons for the divergence between reductionist and organicist accounts and seems to suggest that the basis for distinctions among causal relationships that scientists sometimes make can be found in the hierarchical character of complex biological systems. I will argue that these different causal relationships reflect different levels of epistemic concern.