Medicine and the call for a moral epistemology

For over a century, medicine has prided itself on its scientific orientation and technological accomplishments. But a conceptual crack lies at the foundation of contemporary medicine, one that may be characterized as a conflict between medicine's scientific epistemology and its moral philosophy. Moral refers to value, and more specifically in the clinical setting, to how facts must be ordered by the values attached to them. A "moral epistemology" seeks to bring these two domains into closer proximity. Clinical facts always reside in a complex array of systems that confer specific and often unique meanings to any finding. An integration of unsteady norms and the intuitive inference arising from the individuality of disease expression require that judgments order facts into their proper placement. And beyond this relaxed view of objectivity, clinical care must also incorporate judgments arising from the patient's (as well as the physician's) social and psychological realms that are removed from scientific concerns. Together, these various kinds of value judgments erect the scaffold of clinical care, in which a more complex moral epistemology emerges. A comprehensive biopsychosocial model of illness and its treatment articulates this integrated orientation, but until medicine embraces a philosophy that legitimates the full integration of facts and values, the appeal of such an approach will remain limited and its application ineffective.     

Mutations, evolutionary theory and cancer

When searching for mutations that may be responsible for tumourigenesis and interpreting their significance, molecular oncologists often make a number of implicit assumptions about how and why tumour genotypes develop. These assumptions are based on an underlying classical model of tumourigenesis. The classical model has a number of similarities to models of evolution: given the parallels between the growth of tumours and the evolution of whole organisms, this is to be expected. However, consideration of tumourigenesis as an evolutionary process also suggests some modifications that might be made to the classical model. The experimental methods and data analysis of molecular oncology must take full account of the potential contribution of evolutionary theory. As the study of mutations in cancer expands, molecular oncologists are starting to do this.     

Using genetic networks and homology to understand the evolution of phenotypic traits

Homology can have different meanings for different kinds of biologists. A phylogenetic view holds that homology, defined by common ancestry, is rigorously identified through phylogenetic analysis. Such homologies are taxic homologies (=synapomorphies). A second interpretation, "biological homology" emphasizes common ancestry through the continuity of genetic information underlying phenotypic traits, and is favored by some developmental geneticists. A third kind of homology, deep homology, was recently defined as "the sharing of the genetic regulatory apparatus used to build morphologically and phylogenetically disparate features." Here we explain the commonality among these three versions of homology. We argue that biological homology, as evidenced by a conserved gene regulatory network giving a trait its "essential identity" (a Character Identity Network or "ChIN") must also be a taxic homology. In cases where a phenotypic trait has been modified over the course of evolution such that homology (taxic) is obscured (e.g. jaws are modified gill arches), a shared underlying ChIN provides evidence of this transformation. Deep homologies, where molecular and cellular components of a phenotypic trait precede the trait itself (are phylogenetically deep relative to the trait), are also taxic homologies, undisguised. Deep homologies inspire particular interest for understanding the evolutionary assembly of phenotypic traits. Mapping these deeply homologous building blocks on a phylogeny reveals the sequential steps leading to the origin of phenotypic novelties. Finally, we discuss how new genomic technologies will revolutionize the comparative genomic study of non-model organisms in a phylogenetic context, necessary to understand the evolution of phenotypic traits.     

How to Build a Multiscale Model in Biology

Biological processes span several scales in space, from the single molecules to organisms and ecosystems. Multiscale modelling approaches in biology are useful to take into account the complex interactions between different organisation levels in those systems. We review several single- and multiscale models, from the most simple to the complex ones, and discuss their properties from a multiscale point of view. Approaches based on master equations for stochastic processes, individual-based models, hybrid continuous-discrete models and structured PDE models are presented.     

Modeling transmission of directly transmitted infectious diseases using colored stochastic Petri nets

In order to improve our understanding of directly transmitted pathogens within host populations, epidemic models should take into account individual heterogeneities as well as stochastic fluctuations in individual parameters. The associated cost results in an increasing level of complexity of the mathematical models which generally lack consistent formalisms. In this paper, we demonstrate that complex epidemic models could be expressed as colored stochastic Petri nets (CSPN). CSPN is a mathematical tool developed in computer science. The concept is based on the Markov Chain theory and on a standard well codified graphical formalism. This approach presents an alternative to other computer simulation methods since it offers both a theoretical formalism and a graphical representation that facilitate the implementation, the understanding and thus the replication or modification of the model. We explain how common concepts of epidemic models--such as the incidence function--can be easily translated into an individual based point of view in the CSPN formalism. We then illustrate this approach by using the well documented susceptible-infected model with recruitment and death.     

Estimating differential reproductive success from nests of related individuals, with application to a study of the mottled sculpin, Cottus bairdi

Understanding how variation in reproductive success is related to demography is a critical component in understanding the life history of an organism. Parentage analysis using molecular markers can be used to estimate the reproductive success of different groups of individuals in natural populations. Previous models have been developed for cases where offspring are random samples from the population but these models do not account for the presence of full- and half-sibs commonly found in large clutches of many organisms. Here we develop a model for comparing reproductive success among different groups of individuals that explicitly incorporates within-nest relatedness. Inference for the parameters of the model is done in a Bayesian framework, where we sample from the joint posterior of parental assignments and fertility parameters. We use computer simulations to determine how well our model recovers known parameters and investigate how various data collection scenarios (varying the number of nests or the number of offspring) affect the estimates. We then apply our model to compare reproductive success among different age groups of mottled sculpin, Cottus bairdi, from a natural population. We demonstrate that older adults are more likely to contribute to a nest and that females in the older age groups contribute more eggs to a nest than younger individuals.     

Sustainability of culture-driven population dynamics

We consider models of the interactions between human population dynamics and cultural evolution, asking whether they predict sustainable or unsustainable patterns of growth. Phenomenological models predict either unsustainable population growth or stabilization in the near future. The latter prediction, however, is based on extrapolation of current demographic trends and does not take into account causal processes of demographic and cultural dynamics. Most existing causal models assume (or derive from simplified models of the economy) a positive feedback between cultural evolution and demographic growth, and predict unlimited growth in both culture and population. We augment these models taking into account that: (1) cultural transmission is not perfect, i.e., culture can be lost; (2) culture does not always promote population growth. We show that taking these factors into account can cause radically different model behavior, such as population extinction rather than stability, and extinction rather than growth. We conclude that all models agree that a population capable of maintaining a large amount of culture, including a powerful technology, runs a high risk of being unsustainable. We suggest that future work must address more explicitly both the dynamics of resource consumption and the cultural evolution of beliefs implicated in reproductive behavior (e.g., ideas about the preferred family size) and in resource use (e.g., environmentalist stances).     

A Mathematical Model of Force Generation by Flexible Kinetochore-Microtubule Attachments

Important mechanical events during mitosis are facilitated by the generation of force by chromosomal kinetochore sites that attach to dynamic microtubule tips. Several theoretical models have been proposed for how these sites generate force, and molecular diffusion of kinetochore components has been proposed as a key component that facilitates kinetochore function. However, these models do not explicitly take into account the recently observed flexibility of kinetochore components and variations in microtubule shape under load. In this paper, we develop a mathematical model for kinetochore-microtubule connections that directly incorporates these two important components, namely, flexible kinetochore binder elements, and the effects of tension load on the shape of shortening microtubule tips. We compare our results with existing biased diffusion models and explore the role of protein flexibility inforce generation at the kinetochore-microtubule junctions. Our model results suggest that kinetochore component flexibility and microtubule shape variation under load significantly diminish the need for high diffusivity (or weak specific binding) of kinetochore components; optimal kinetochore binder stiffness regimes are predicted by our model. Based on our model results, we suggest that the underlying principles of biased diffusion paradigm need to be reinterpreted.     

Animal models and conserved processes

Background

The concept of conserved processes presents unique opportunities for using nonhuman animal models in biomedical research. However, the concept must be examined in the context that humans and nonhuman animals are evolved, complex, adaptive systems. Given that nonhuman animals are examples of living systems that are differently complex from humans, what does the existence of a conserved gene or process imply for inter-species extrapolation?

Methods

We surveyed the literature including philosophy of science, biological complexity, conserved processes, evolutionary biology, comparative medicine, anti-neoplastic agents, inhalational anesthetics, and drug development journals in order to determine the value of nonhuman animal models when studying conserved processes.

Results

Evolution through natural selection has employed components and processes both to produce the same outcomes among species but also to generate different functions and traits. Many genes and processes are conserved, but new combinations of these processes or different regulation of the genes involved in these processes have resulted in unique organisms. Further, there is a hierarchy of organization in complex living systems. At some levels, the components are simple systems that can be analyzed by mathematics or the physical sciences, while at other levels the system cannot be fully analyzed by reducing it to a physical system. The study of complex living systems must alternate between focusing on the parts and examining the intact whole organism while taking into account the connections between the two. Systems biology aims for this holism. We examined the actions of inhalational anesthetic agents and anti-neoplastic agents in order to address what the characteristics of complex living systems imply for inter-species extrapolation of traits and responses related to conserved processes.

Conclusion

We conclude that even the presence of conserved processes is insufficient for inter-species extrapolation when the trait or response being studied is located at higher levels of organization, is in a different module, or is influenced by other modules. However, when the examination of the conserved process occurs at the same level of organization or in the same module, and hence is subject to study solely by reductionism, then extrapolation is possible.     

Modeling the presence probability of invasive plant species with nonlocal dispersal

Mathematical models for the spread of invading plant organisms typically utilize population growth and dispersal dynamics to predict the time-evolution of a population distribution. In this paper, we revisit a particular class of deterministic contact models obtained from a stochastic birth process for invasive organisms. These models were introduced by Mollison (J R Stat Soc 39(3):283, 1977). We derive the deterministic integro-differential equation of a more general contact model and show that the quantity of interest may be interpreted not as population size, but rather as the probability of species occurrence. We proceed to show how landscape heterogeneity can be included in the model by utilizing the concept of statistical habitat suitability models which condense diverse ecological data into a single statistic. As ecologists often deal with species presence data rather than population size, we argue that a model for probability of occurrence allows for a realistic determination of initial conditions from data. Finally, we present numerical results of our deterministic model and compare them to simulations of the underlying stochastic process.     

A general multivariate extension of Fisher's geometrical model and the distribution of mutation fitness effects across species

The evolution of complex organisms is a puzzle for evolutionary theory because beneficial mutations should be less frequent in complex organisms, an effect termed "cost of complexity." However, little is known about how the distribution of mutation fitness effects (f(s)) varies across genomes. The main theoretical framework to address this issue is Fisher's geometric model and related phenotypic landscape models. However, it suffers from several restrictive assumptions. In this paper, we intend to show how several of these limitations may be overcome. We then propose a model of f(s) that extends Fisher's model to account for arbitrary mutational and selective interactions among n traits. We show that these interactions result in f(s) that would be predicted by a much smaller number of independent traits. We test our predictions by comparing empirical f(s) across species of various gene numbers as a surrogate to complexity. This survey reveals, as predicted, that mutations tend to be more deleterious, less variable, and less skewed in higher organisms. However, only limited difference in the shape of f(s) is observed from Escherichia coli to nematodes or fruit flies, a pattern consistent with a model of random phenotypic interactions across many traits. Overall, these results suggest that there may be a cost to phenotypic complexity although much weaker than previously suggested by earlier theoretical works. More generally, the model seems to qualitatively capture and possibly explain the variation of f(s) from lower to higher organisms, which opens a large array of potential applications in evolutionary genetics.     

Spatial variability in species' potential distributions during the Last Glacial Maximum under different Global Circulation Models: Relevance in evolutionary biology

Ecological niche models (ENM) have been used to reconstruct potential distributions during the Last Glacial Maximum (LGM)—or other time periods—and this use is increasingly common in zoological studies. For this reason, we urgently need understanding factors affecting these predictions. Here, we examine how the use of different Global Circulation Models (GCMs) affects the variability in species' potential distributions during the LGM and how the degree of model extrapolation and its associated uncertainty depends on the GCM used. We develop these issues using two North American shrews, Notiosorex crawfordi and Cryptotis alticola, inhabiting two environmentally different regions. First, we compared paleoclimates in these two regions simulated by three GCMs: Community Climate System Model (CCSM), Model for Interdisciplinary Research on Climate (MIROC), and the Max‐Planck‐Institute für Meteorologie model (MPI). Then, we used maxent to estimate the LGM potential distribution of these two mammals under the three GCMs to assess the spatial variability and extrapolation uncertainty associated with idiosyncrasies of GCM. MIROC estimated noticeably more different climatic conditions than CCSM and MPI in the study areas during the LGM, and its pattern of environmental conditions was distributed differently. The MIROC scenario suggested a remarkable different prediction of potential distribution for both species, being more dramatic for the high mountain shrew, C. alticola. In particular, climatic differences among GCMs resulted in differences in the factors that limit and drive the potential distribution of the species during the LGM. Equally dramatic was the disagreement of extrapolation areas among GCMs. MIROC showed a greater number of pixels where extrapolation is required in both regions. Our findings should be taken into consideration when identifying areas of endemism, dynamic geographic barriers, and glacial refugia. When projecting into alternative scenarios of LGM climate, the idiosyncrasies of each GCM should be explicitly taken into account.     

The utility of behavioral models and modules in molecular analyses of social behavior

It is extremely difficult to trace the causal pathway relating gene products or molecular pathways to the expression of behavior. This is especially true for social behavior, which being dependent on interactions and communication between individuals is even further removed from molecular-level events. In this review, we discuss how behavioral models can aid molecular analyses of social behavior. Various models of behavior exist, each of which suggest strategies to dissect complex behavior into simpler behavioral 'modules.' The resulting modules are easier to relate to neural processes and thus suggest hypotheses for neural and molecular function. Here we discuss how three different models of behavior have facilitated understanding the molecular bases of aspects of social behavior. We discuss the response threshold model and two different approaches to modeling motivation, the state space model and models of reinforcement and reward processing. The examples we have chosen illustrate how models can generate testable hypotheses for neural and molecular function and also how molecular analyses probe the validity of a model of behavior. We do not champion one model over another; rather, our examples illustrate how modeling and molecular analyses can be synergistic in exploring the molecular bases of social behavior.     

Interspecies extrapolation based on mechanistic determinants of chemical disposition

Uncertainty factors are applied in methods developed by the Environmental Protection Agency (EPA) to derive dose‐response estimates. The uncertainty factors are applied to account for uncertainties in defined extrapolations from the laboratory animal experimental data conditions to a dose‐response estimate appropriate for the assumed human scenario. The conceptual difference between these uncertainty factors and safety factors is best illustrated by how uncertainty factors can be modified as scientific data inform our understanding of the key factors that influence chemical disposition and toxicity. Mechanistic data help describe the major factors influencing chemical disposition and toxicant‐target tissue interactions, and should increase the accuracy of exposure‐dose‐response assessment. Mechanistic data on the determinants of inhaled chemical disposition were used to construct default dosimetry adjustments applied by the EPA in its inhalation Reference Concentration (RfC) methods. Because these adjustments account for interspecies dosimetric differences to some degree, the uncertainty factor for interspecies extrapolation was modified. A framework is presented that allows for incorporation of mechanistic data in order to ensure that required extrapolations are commensurate with the state‐of‐the‐science. Future applications of mechanistic data to modify additional uncertainty factors are outlined.     

The history of the homology concept and the “Phylogenetisches Symposium”

The homology concept has had a long and varied history, starting out as a geometrical term in ancient Greece. Here we describe briefly how a typological use of homology to designate organs and body parts in the same position anatomically in different organisms was changed by Darwin’s theory of evolution into a phylogenetic concept. We try to indicate the diversity of opinions on how to define and test for homology that has prevailed historically, before the important books by Hennig (1950. Grundzüge einer Theorie der Phylogenetischen Systematik. Deutscher Zentralverlag, Berlin) and Remane (1952. Die Grundlagen des Natürlichen Systems, der Vergleichenden Anatomie und der Phylogenetik. Geest & Portig, Leipzig) brought more rigor into both the debate on homology and into the usage of the term homology among systematists. Homology as a theme has recurred repeatedly throughout the history of the “Phylogenetisches Symposium” and we give a very brief overview of the different aspects of homology that have been discussed at specific symposia over the last 48 years. We also honour the fact that the 2004 symposium was held in Jena by pointing to the roles played by biologists active in Jena, such as Ernst Haeckel and Carl Gegenbaur, in starting the development towards a homology concept concordant with an evolutionary world view. As historians of biology, we emphasize the importance of major treatises on homology and its history that may be little read by systematists active today, and have sometimes also received less attention by historians of biology than they deserve. Prominent among these are the works of Dietrich Starck, who also happened to be both a student, and later a benefactor, of systematics at Jena University.     

A Mathematical Model of Force Generation by Flexible Kinetochore-Microtubule Attachments

Important mechanical events during mitosis are facilitated by the generation of force by chromosomal kinetochore sites that attach to dynamic microtubule tips. Several theoretical models have been proposed for how these sites generate force, and molecular diffusion of kinetochore components has been proposed as a key component that facilitates kinetochore function. However, these models do not explicitly take into account the recently observed flexibility of kinetochore components and variations in microtubule shape under load. In this paper, we develop a mathematical model for kinetochore-microtubule connections that directly incorporates these two important components, namely, flexible kinetochore binder elements, and the effects of tension load on the shape of shortening microtubule tips. We compare our results with existing biased diffusion models and explore the role of protein flexibility inforce generation at the kinetochore-microtubule junctions. Our model results suggest that kinetochore component flexibility and microtubule shape variation under load significantly diminish the need for high diffusivity (or weak specific binding) of kinetochore components; optimal kinetochore binder stiffness regimes are predicted by our model. Based on our model results, we suggest that the underlying principles of biased diffusion paradigm need to be reinterpreted.     

Expression, purification, and characterization of cytokinin signaling intermediates: Arabidopsis histidine phosphotransfer protein 1 (AHP1) and AHP2

Key message

We have expressed, purified, and biophysically characterized recombinant AHP1 and AHP2. Also, using computational homology models for AHP1, ARR7, and AHP1–ARR7 complex, we identified three-dimensional positioning of key amino acids.

Abstract

Cytokinin signaling involves activation of Arabidopsis Response Regulators (ARRs) by Arabidopsis Histidine Phosphotransfer Proteins (AHPs) by phosphorylation. Type-A ARRs are key regulators of several developmental pathways, but the mechanism underlying this phosphorylation and activation is not known in plants. In this study, we report the successful expression and purification of recombinant AHP1 and AHP2. Biophysical characterization shows that these two recombinant proteins were purified to homogeneity and possess well-defined secondary structures. Brief attempts to purify recombinant ARR7 posed problems during size-exclusion chromatography. Nevertheless, we generated computational homology models for AHP1, ARR7, and AHP1–ARR7 complex using crystal structures of homologous proteins from other organisms. The homology models helped to identify the three-dimensional positioning of the key conserved residues of AHP1 and ARR7 involved in phosphorylation. The similarity in positioning of these residues to other homologous proteins suggests that AHPs and type-A ARRs could be structurally conserved across kingdoms. Thus, our homology models can serve as valuable tools to gain structural insights into the phosphorylation and activation of cytokinin response regulators in plants.     

Prime, repair, restore: the active role of chromatin in the DNA damage response

The view of DNA packaging into chromatin as a mere obstacle to DNA repair is evolving. In this review, we focus on histone variants and heterochromatin proteins as chromatin components involved in distinct levels of chromatin organization to integrate them as real players in the DNA damage response (DDR). Based on recent data, we highlight how some of these chromatin components play active roles in the DDR and contribute to the fine-tuning of damage signaling, DNA and chromatin repair. To take into account this integrated view, we revisit the existing access-repair-restore model and propose a new working model involving priming chromatin for repair and restoration as a concerted process. We discuss how this impacts on both genomic and epigenomic stability and plasticity.     

Functional mapping of ontogeny in flowering plants

All organisms face the problem of how to perform a sequence of developmental changes and transitions during ontogeny. We revise functional mapping, a statistical model originally derived to map genes that determine developmental dynamics, to take into account the entire process of ontogenetic growth from embryo to adult and from the vegetative to reproductive phase. The revised model provides a framework that reconciles the genetic architecture of development at different stages and elucidates a comprehensive picture of the genetic control mechanisms of growth that change gradually from a simple to a more complex level. We use an annual flowering plant, as an example, to demonstrate our model by which to map genes and their interactions involved in embryo and postembryonic growth. The model provides a useful tool to study the genetic control of ontogenetic growth in flowering plants and any other organisms through proper modifications based on their biological characteristics.     

Towards classifying species in systems biology papers using text mining

Background

In recent years high throughput methods have led to a massive expansion in the free text literature on molecular biology. Automated text mining has developed as an application technology for formalizing this wealth of published results into structured database entries. However, database curation as a task is still largely done by hand, and although there have been many studies on automated approaches, problems remain in how to classify documents into top-level categories based on the type of organism being investigated. Here we present a comparative analysis of state of the art supervised models that are used to classify both abstracts and full text articles for three model organisms.

Results

Ablation experiments were conducted on a large gold standard corpus of 10,000 abstracts and full papers containing data on three model organisms (fly, mouse and yeast). Among the eight learner models tested, the best model achieved an F-score of 97.1% for fly, 88.6% for mouse and 85.5% for yeast using a variety of features that included gene name, organism frequency, MeSH headings and term-species associations. We noted that term-species associations were particularly effective in improving classification performance. The benefit of using full text articles over abstracts was consistently observed across all three organisms.

Conclusions

By comparing various learner algorithms and features we presented an optimized system that automatically detects the major focus organism in full text articles for fly, mouse and yeast. We believe the method will be extensible to other organism types.