Free fitness that always increases in evolution

I here introduce a free fitness function in population biology, which monotonically increases with time and takes its maximum at the evolutionary equilibrium. By suitably defining an "index" for each state, the free fitness is expressed as the average index plus an entropy term. In many cases, the index has a biologically clear meaning, such as the logarithmic population mean fitness. The technique is applicable to any Markov process model (either continuous or discrete) with a positive steady state. I discuss four examples from various branches of population biology: (1) one-locus-two-allele system of population genetics with mutation, selection, and random genetic drift; (2) evolutionary dynamics of quantitative characters; (3) a molecular evolution model; and (4) an ecological succession model. Introducing free fitness clarifies the balance between systematic forces (e.g. natural selection or successional trend toward the climax) and disturbing processes (e.g. random drift).     

Biophysical limits on athermal effects of RF and microwave radiation

Using biophysical criteria, I show that continuous radiofrequency (RF) and microwave radiation with intensity less than 10 mW/cm(2) are unlikely to affect physiology significantly through athermal mechanisms. Biological systems are fundamentally noisy on the molecular scale as a consequence of thermal agitation and are noisy macroscopically as a consequence of physiological functions and animal behavior. If electromagnetic fields are to significantly affect physiology, their direct physical effect must be greater than that from the ubiquitous endogenous noise. Using that criterion, I show that none of a set of interactions of weak fields, which I argue is nearly complete on dimensional grounds, can affect biology on the molecular scale. Moreover, I conclude that such weak fields are quite unlikely to generate significant effects in their interactions with larger biological elements such as cells. In the course of that analysis, I examine important special examples of electromagnetic interactions: "direct" interactions where biology is modified simply by the motion of charged elements generated by the electric field; resonance interactions; the effects of electrostrictive forces and induced dipole moments; and modifications of radical pair recombination probabilities. In each case, I show that it is unlikely that low intensity fields can generate significant physiological consequences.     

Tools and challenges for diversity-driven proteomics in Brazil

Our current knowledge in biology has been mostly derived from studying model organisms and cell lines in which only a small fraction of all described species have been extensively studied. Although these model organisms are amenable to genetic manipulations, this blinds researchers to the true variability of life. Groundbreaking discoveries are often achieved by analyzing "noncanonical" species; for example, the characterization of Taq polymerase from Thermus aquaticus ultimately led to a revolution in the field of molecular biology. Brazil possesses a rich biodiversity and a considerable fraction of Brazilian groups use current proteomic techniques to explore this natural treasure-trove. However, in our opinion, much more than the widely adopted peptide spectrum match approach is required to explore this rich "proteomosphere." Here, we provide a critical overview of the available strategies for the analysis of proteomic data from "noncanonical" biological samples (e.g. proteins from unsequenced genomes or genomes with high levels of polymorphisms), and demonstrate some limitations of existing approaches for large-scale protein identification and quantitation. An understanding of the premises behind these computational tools is necessary to properly deal with their limitations and draw accurate conclusions.     

Macronuclear Genome Sequence of the Ciliate Tetrahymena thermophila, a Model Eukaryote

The ciliate Tetrahymena thermophila is a model organism for molecular and cellular biology. Like other ciliates, this species has separate germline and soma functions that are embodied by distinct nuclei within a single cell. The germline-like micronucleus (MIC) has its genome held in reserve for sexual reproduction. The soma-like macronucleus (MAC), which possesses a genome processed from that of the MIC, is the center of gene expression and does not directly contribute DNA to sexual progeny. We report here the shotgun sequencing, assembly, and analysis of the MAC genome of T. thermophila, which is approximately 104 Mb in length and composed of approximately 225 chromosomes. Overall, the gene set is robust, with more than 27,000 predicted protein-coding genes, 15,000 of which have strong matches to genes in other organisms. The functional diversity encoded by these genes is substantial and reflects the complexity of processes required for a free-living, predatory, single-celled organism. This is highlighted by the abundance of lineage-specific duplications of genes with predicted roles in sensing and responding to environmental conditions (e.g., kinases), using diverse resources (e.g., proteases and transporters), and generating structural complexity (e.g., kinesins and dyneins). In contrast to the other lineages of alveolates (apicomplexans and dinoflagellates), no compelling evidence could be found for plastid-derived genes in the genome. UGA, the only T. thermophila stop codon, is used in some genes to encode selenocysteine, thus making this organism the first known with the potential to translate all 64 codons in nuclear genes into amino acids. We present genomic evidence supporting the hypothesis that the excision of DNA from the MIC to generate the MAC specifically targets foreign DNA as a form of genome self-defense. The combination of the genome sequence, the functional diversity encoded therein, and the presence of some pathways missing from other model organisms makes T. thermophila an ideal model for functional genomic studies to address biological, biomedical, and biotechnological questions of fundamental importance.     

Macronuclear genome sequence of the ciliate Tetrahymena thermophila, a model eukaryote

The ciliate Tetrahymena thermophila is a model organism for molecular and cellular biology. Like other ciliates, this species has separate germline and soma functions that are embodied by distinct nuclei within a single cell. The germline-like micronucleus (MIC) has its genome held in reserve for sexual reproduction. The soma-like macronucleus (MAC), which possesses a genome processed from that of the MIC, is the center of gene expression and does not directly contribute DNA to sexual progeny. We report here the shotgun sequencing, assembly, and analysis of the MAC genome of T. thermophila, which is approximately 104 Mb in length and composed of approximately 225 chromosomes. Overall, the gene set is robust, with more than 27,000 predicted protein-coding genes, 15,000 of which have strong matches to genes in other organisms. The functional diversity encoded by these genes is substantial and reflects the complexity of processes required for a free-living, predatory, single-celled organism. This is highlighted by the abundance of lineage-specific duplications of genes with predicted roles in sensing and responding to environmental conditions (e.g., kinases), using diverse resources (e.g., proteases and transporters), and generating structural complexity (e.g., kinesins and dyneins). In contrast to the other lineages of alveolates (apicomplexans and dinoflagellates), no compelling evidence could be found for plastid-derived genes in the genome. UGA, the only T. thermophila stop codon, is used in some genes to encode selenocysteine, thus making this organism the first known with the potential to translate all 64 codons in nuclear genes into amino acids. We present genomic evidence supporting the hypothesis that the excision of DNA from the MIC to generate the MAC specifically targets foreign DNA as a form of genome self-defense. The combination of the genome sequence, the functional diversity encoded therein, and the presence of some pathways missing from other model organisms makes T. thermophila an ideal model for functional genomic studies to address biological, biomedical, and biotechnological questions of fundamental importance.     

‘Lassoing’ a phylogenetic tree I: basic properties, shellings, and covers

A classical result, fundamental to evolutionary biology, states that an edge-weighted tree T with leaf set X, positive edge weights, and no vertices of degree 2 can be uniquely reconstructed from the leaf-to-leaf distances between any two elements of X. In biology, X corresponds to a set of taxa (e.g. extant species), the tree T describes their phylogenetic relationships, the edges correspond to earlier species evolving for a time until splitting in two or more species by some speciation/bifurcation event, and their length corresponds to the genetic change accumulating over that time in such a species. In this paper, we investigate which subsets of ${\binom{X}{2}}$ suffice to determine (??lasso??) the tree T from the leaf-to-leaf distances induced by that tree. The question is particularly topical since reliable estimates of genetic distance??even (if not in particular) by modern mass-sequencing methods??are, in general, available only for certain combinations of taxa.     

Teaching systems biology: an active-learning approach

With genomics well established in modern molecular biology, recent studies have sought to further the discipline by integrating complementary methodologies into a holistic depiction of the molecular mechanisms underpinning cell function. This genomic subdiscipline, loosely termed "systems biology," presents the biology educator with both opportunities and obstacles: The benefit of exposing students to this cutting-edge scientific methodology is manifest, yet how does one convey the breadth and advantage of systems biology while still engaging the student? Here, I describe an active-learning approach to the presentation of systems biology. In graduate classes at the University of Michigan, Ann Arbor, I divided students into small groups and asked each group to interpret a sample data set (e.g., microarray data, two-hybrid data, homology-search results) describing a hypothetical signaling pathway. Mimicking realistic experimental results, each data set revealed a portion of this pathway; however, students were only able to reconstruct the full pathway by integrating all data sets, thereby exemplifying the utility in a systems biology approach. Student response to this cooperative exercise was extremely positive. In total, this approach provides an effective introduction to systems biology appropriate for students at both the undergraduate and graduate levels.     

Nonparametric estimation of stage occupation probabilities in a multistage model with current status data

Multistage models are used to describe individuals (or experimental units) moving through a succession of "stages" corresponding to distinct states (e.g., healthy, diseased, diseased with complications, dead). The resulting data can be considered to be a form of multivariate survival data containing information about the transition times and the stages occupied. Traditional survival analysis is the simplest example of a multistage model, where individuals begin in an initial stage (say, alive) and move irreversibly to a second stage (death). In this article, we consider general multistage models with a directed tree structure (progressive models) in which individuals traverse through stages in a possibly non-Markovian manner. We construct nonparametric estimators of stage occupation probabilities and marginal cumulative transition hazards. Empirical calculations of these quantities are not possible due to the lack of complete data. We consider current status information which represents a more severe form of censoring than the commonly used right censoring. Asymptotic validity of our estimators can be justified using consistency results for nonparametric regression estimators. Finite-sample behavior of our estimators is studied by simulation, in which we show that our estimators based on these limited data compare well with those based on complete data. We also apply our method to a real-life data set arising from a cardiovascular diseases study in Taiwan.     

Genome, evolution, Drosophila and beyond: the new dimensions

A century ago a little fly with red eyes was first used for genetic studies. That insignificant fly, called at that time Drosophila ampelophila, revolutionized biology while becoming the model we know today under the name of Drosophila melanogaster. Since then its study has never ceased, but the field of interest has somewhat changed during the century. To caricature a little, today we essentially learn from Drosophila meetings that the fly has a brain! It is true that the fly is a tremendous model organism for neurobiology. But this fly is, in fact, an appropriate and recognized model for the whole of biology. Indeed, Drosophila meetings are exceptional opportunities to gather biologists of diverse backgrounds together. There we not only learn about the latest improvements in our field of interest, but surely appreciate learning another bit of biology. From this biological melting pot has emerged a culture very specific to the fly community. Thus besides neurobiology, cell biology and development, a diversity of other research fields exist; they all have their own place in the cultural and historical dimension of the "drosophila" model. Several communications from those diverse research fields were presented at the 8th Japanese Drosophila Research Conference (JDRC8) and are briefly covered here. We believe it more judicious to call the model "drosophila" without a capital initial, as the model has never really been limited to only the Drosophila genus. The vernacular name "drosophila" is currently used to designate any fly of the Drosophilidae family and we believe the term more appropriate than "small fruit fly" or "vinegar fly" to better include the species and ecological diversity of the model.     

The cell as the smallest DNA-based molecular computer

The pioneering work of Adleman (1994) demonstrated that DNA molecules in test tubes can be manipulated to perform a certain type of mathematical computation. This has stimulated a theoretical interest in the possibility of constructing DNA-based molecular computers. To gauge the practicality of realizing such microscopic computers, it was thought necessary to learn as much as possible from the biology of the living cell--presently the only known DNA-based molecular computer in existence. Here the recently developed theoretical model of the living cell (the Bhopalator) and its associated theories (e.g. cell language), principles, laws and concepts (e.g. conformons, IDS's) are briefly reviewed and summarized in the form of a set of five laws of 'molecular semiotics' (synonyms include 'microsemiotics', 'cellular semiotics', or 'cytosemiotics') the study of signs mediating measurement, computation, and communication on the cellular and molecular levels. Hopefully, these laws will find practical applications in designing DNA-based computing systems.     

Yeast: an experimental organism for 21st Century biology

In this essay, we revisit the status of yeast as a model system for biology. We first summarize important contributions of yeast to eukaryotic biology that we anticipated in 1988 in our first article on the subject. We then describe transformative developments that we did not anticipate, most of which followed the publication of the complete genomic sequence of Saccharomyces cerevisiae in 1996. In the intervening 23 years it appears to us that yeast has graduated from a position as the premier model for eukaryotic cell biology to become the pioneer organism that has facilitated the establishment of the entirely new fields of study called "functional genomics" and "systems biology." These new fields look beyond the functions of individual genes and proteins, focusing on how these interact and work together to determine the properties of living cells and organisms.     

Proliferation and Death in a Binary Environment: A Stochastic Model of Cellular Ecosystems

The activation, growth and death of animal cells are accompanied by changes in the chemical composition of the surrounding environment. Cells and their microscopic environment constitute therefore a cellular ecosystem whose time-evolution determines processes of interest for either biology (e.g. animal development) and medicine (e.g. tumor spreading, immune response). In this paper, we consider a general stochastic model of the interplay between cells and environmental cellular niches. Niches may be either favourable or unfavourable in sustaining cell activation, growth and death, the state of the niches depending on the state of the cells. Under the hypothesis of random coupling between the state of the environmental niche and the state of the cell, the rescaled model reduces to a set of four non-linear differential equations. The biological meaning of the model is studied and illustrated by fitting experimental data on the growth of multicellular tumor spheroids. A detailed analysis of the stochastic model, of its deterministic limit, and of normal fluctuations is provided.     

Network neighborhood analysis with the multi-node topological overlap measure

MOTIVATION: The goal of neighborhood analysis is to find a set of genes (the neighborhood) that is similar to an initial 'seed' set of genes. Neighborhood analysis methods for network data are important in systems biology. If individual network connections are susceptible to noise, it can be advantageous to define neighborhoods on the basis of a robust interconnectedness measure, e.g. the topological overlap measure. Since the use of multiple nodes in the seed set may lead to more informative neighborhoods, it can be advantageous to define multi-node similarity measures. RESULTS: The pairwise topological overlap measure is generalized to multiple network nodes and subsequently used in a recursive neighborhood construction method. A local permutation scheme is used to determine the neighborhood size. Using four network applications and a simulated example, we provide empirical evidence that the resulting neighborhoods are biologically meaningful, e.g. we use neighborhood analysis to identify brain cancer related genes. AVAILABILITY: An executable Windows program and tutorial for multi-node topological overlap measure (MTOM) based analysis can be downloaded from the webpage (http://www.genetics.ucla.edu/labs/horvath/MTOM/).     

Fossil evidence for key innovations in the evolution of insect diversity

Explaining the taxonomic richness of the insects, comprising over half of all described species, is a major challenge in evolutionary biology. Previously, several evolutionary novelties (key innovations) have been posited to contribute to that richness, including the insect bauplan, wings, wing folding and complete metamorphosis, but evidence over their relative importance and modes of action is sparse and equivocal. Here, a new dataset on the first and last occurrences of fossil hexapod (insects and close relatives) families is used to show that basal families of winged insects (Palaeoptera, e.g. dragonflies) show higher origination and extinction rates in the fossil record than basal wingless groups (Apterygota, e.g. silverfish). Origination and extinction rates were maintained at levels similar to Palaeoptera in the more derived Polyneoptera (e.g. cockroaches) and Paraneoptera (e.g. true bugs), but extinction rates subsequently reduced in the very rich group of insects with complete metamorphosis (Holometabola, e.g. beetles). Holometabola show evidence of a recent slow-down in their high net diversification rate, whereas other winged taxa continue to diversify at constant but low rates. These data suggest that wings and complete metamorphosis have had the most effect on family-level insect macroevolution, and point to specific mechanisms by which they have influenced insect diversity through time.     

Why don't zebras have machine guns? Adaptation, selection, and constraints in evolutionary theory

In an influential paper, Stephen Jay Gould and Richard Lewontin (1979) contrasted selection-driven adaptation with phylogenetic, architectural, and developmental constraints as distinct causes of phenotypic evolution. In subsequent publications Gould (e.g., 1997a,b, 2002) has elaborated this distinction into one between a narrow "Darwinian Fundamentalist" emphasis on "external functionalist" processes, and a more inclusive "pluralist" emphasis on "internal structuralist" principles. Although theoretical integration of functionalist and structuralist explanations is the ultimate aim, natural selection and internal constraints are treated as distinct causes of evolutionary change. This distinction is now routinely taken for granted in the literature in evolutionary biology. I argue that this distinction is problematic because the effects attributed to non-selective constraints are more parsimoniously explained as the ordinary effects of selection itself. Although it may still be a useful shorthand to speak of phylogenetic, architectural, and developmental constraints on phenotypic evolution, it is important to understand that such "constraints" do not constitute an alternative set of causes of evolutionary change. The result of this analysis is a clearer understanding of the relationship between adaptation, selection and constraints as explanatory concepts in evolutionary theory.     

A review of cell assemblies

Since the cell assembly (CA) was hypothesised, it has gained substantial support and is believed to be the neural basis of psychological concepts. A CA is a relatively small set of connected neurons, that through neural firing can sustain activation without stimulus from outside the CA, and is formed by learning. Extensive evidence from multiple single unit recording and other techniques provides support for the existence of CAs that have these properties, and that their neurons also spike with some degree of synchrony. Since the evidence is so broad and deep, the review concludes that CAs are all but certain. A model of CAs is introduced that is informal, but is broad enough to include, e.g. synfire chains, without including, e.g. holographic reduced representation. CAs are found in most cortical areas and in some sub-cortical areas, they are involved in psychological tasks including categorisation, short-term memory and long-term memory, and are central to other tasks including working memory. There is currently insufficient evidence to conclude that CAs are the neural basis of all concepts. A range of models have been used to simulate CA behaviour including associative memory and more process- oriented tasks such as natural language parsing. Questions involving CAs, e.g. memory persistence, CAs’ complex interactions with brain waves and learning, remain unanswered. CA research involves a wide range of disciplines including biology and psychology, and this paper reviews literature directly related to the CA, providing a basis of discussion for this interdisciplinary community on this important topic. Hopefully, this discussion will lead to more formal and accurate models of CAs that are better linked to neuropsychological data.     

The layer-oriented approach to declarative languages for biological modeling

We present a new approach to modeling languages for computational biology, which we call the layer-oriented approach. The approach stems from the observation that many diverse biological phenomena are described using a small set of mathematical formalisms (e.g. differential equations), while at the same time different domains and subdomains of computational biology require that models are structured according to the accepted terminology and classification of that domain. Our approach uses distinct semantic layers to represent the domain-specific biological concepts and the underlying mathematical formalisms. Additional functionality can be transparently added to the language by adding more layers. This approach is specifically concerned with declarative languages, and throughout the paper we note some of the limitations inherent to declarative approaches. The layer-oriented approach is a way to specify explicitly how high-level biological modeling concepts are mapped to a computational representation, while abstracting away details of particular programming languages and simulation environments. To illustrate this process, we define an example language for describing models of ionic currents, and use a general mathematical notation for semantic transformations to show how to generate model simulation code for various simulation environments. We use the example language to describe a Purkinje neuron model and demonstrate how the layer-oriented approach can be used for solving several practical issues of computational neuroscience model development. We discuss the advantages and limitations of the approach in comparison with other modeling language efforts in the domain of computational biology and outline some principles for extensible, flexible modeling language design. We conclude by describing in detail the semantic transformations defined for our language.     

Ecosystem evolution: main stages and potential mechanisms

Major differences between the Western and "Russian" (Zavarzin, 1995) paradigms in ecology and evolutionary biology are described. The "Russian" paradigm suggests that there exist two, rather than one, quite independent lineages--species evolution and ecosystem evolution. This is based on the idea that life may exist just as a nutrient cycle. The main terms and concepts of the "Russian" paradigm are defined more exactly. An attempt is made to develop this paradigm so that it would be possible to describe not only phenomenology, but also mechanisms of ecosystem evolution. To simplify evolutionary phenomena logically, it is suggested to use the concept of conditionally complete causal explanation (Lekevicius, 1984; 1985), i.e. deduce evolutionary mechanisms from major principles of functioning. This methodology is adapted to model the main stages of the evolution of nutrient cycles (3.8-2.0 bln. y.a.) and the appearance and evolution of biophagy (1.7-0 bln. y.a.). Based on a multitude of examples, it is shown that these are functional constraints that are the forces directing evolution; those constraints emerge during the interaction of organisms and while the latter interact with the abiotic environment. Since the structure of an ecosystem is non-rigid, each species is able to accumulate features useful to both an ecosystem and itself. Those are individuals that die and reproduce, whereas all structures, from macromolecules to ecosystems, evolve.     

More on how and why: cause and effect in biology revisited

In 1961, Ernst Mayr published a highly influential article on the nature of causation in biology, in which he distinguished between proximate and ultimate causes. Mayr argued that proximate causes (e.g. physiological factors) and ultimate causes (e.g. natural selection) addressed distinct ‘how’ and ‘why’ questions and were not competing alternatives. That distinction retains explanatory value today. However, the adoption of Mayr’s heuristic led to the widespread belief that ontogenetic processes are irrelevant to evolutionary questions, a belief that has (1) hindered progress within evolutionary biology, (2) forged divisions between evolutionary biology and adjacent disciplines and (3) obstructed several contemporary debates in biology. Here we expand on our earlier (Laland et al. in Science 334:1512–1516, 2011) argument that Mayr’s dichotomous formulation has now run its useful course, and that evolutionary biology would be better served by a concept of reciprocal causation, in which causation is perceived to cycle through biological systems recursively. We further suggest that a newer evolutionary synthesis is unlikely to emerge without this change in thinking about causation.