Bridging the explanatory gaps: What can we learn from a biological agency perspective?

We begin this article by delineating the explanatory gaps left by prevailing gene-focused approaches in our understanding of phenotype determination, inheritance, and the origin of novel traits. We aim not to diminish the value of these approaches but to highlight where their implementation, despite best efforts, has encountered persistent limitations. We then discuss how each of these explanatory gaps can be addressed by expanding research foci to take into account biological agency—the capacity of living systems at various levels to participate in their own development, maintenance, and function by regulating their structures and activities in response to conditions they encounter. Here we aim to define formally what agency and agents are and—just as importantly—what they are not, emphasizing that agency is an empirical property connoting neither intention nor consciousness. Lastly, we discuss how incorporating agency helps to bridge explanatory gaps left by conventional approaches, highlight scientific fields in which implicit agency approaches are already proving valuable, and assess the opportunities and challenges of more systematically incorporating biological agency into research programs.     

Linkage disequilibrium and association studies in higher plants: Present status and future prospects

During the last two decades, DNA-based molecular markers have been extensively utilized for a variety of studies in both plant and animal systems. One of the major uses of these markers is the construction of genome-wide molecular maps and the genetic analysis of simple and complex traits. However, these studies are generally based on linkage analysis in mapping populations, thus placing serious limitations in using molecular markers for genetic analysis in a variety of plant systems. Therefore, alternative approaches have been suggested, and one of these approaches makes use of linkage disequilibrium (LD)-based association analysis. Although this approach of association analysis has already been used for studies on genetics of complex traits (including different diseases) in humans, its use in plants has just started. In the present review, we first define and distinguish between LD and association mapping, and then briefly describe various measures of LD and the two methods of its depiction. We then give a list of different factors that affect LD without discussing them, and also discuss the current issues of LD research in plants. Later, we also describe the various uses of LD in plant genomics research and summarize the present status of LD research in different plant genomes. In the end, we discuss briefly the future prospects of LD research in plants, and give a list of softwares that are useful in LD research, which is available as electronic supplementary material (ESM) Electronic supplementary material Electronic supplementary material is available for this article at and accessible for authorised users.     

Global substrate specificity profiling of post‐translational modifying enzymes

Enzymes that modify the proteome, referred to as post‐translational modifying (PTM) enzymes, are central regulators of cellular signaling. Determining the substrate specificity of PTM enzymes is a critical step in unraveling their biological functions both in normal physiological processes and in disease states. Advances in peptide chemistry over the last century have enabled the rapid generation of peptide libraries for querying substrate recognition by PTM enzymes. In this article, we highlight various peptide‐based approaches for analysis of PTM enzyme substrate specificity. We focus on the application of these technologies to proteases and also discuss specific examples in which they have been used to uncover the substrate specificity of other types of PTM enzymes, such as kinases. In particular, we highlight our multiplex substrate profiling by mass spectrometry (MSP‐MS) assay, which uses a rationally designed, physicochemically diverse library of tetradecapeptides. We show how this method has been applied to PTM enzymes to uncover biological function, and guide substrate and inhibitor design. We also briefly discuss how this technique can be combined with other methods to gain a systems‐level understanding of PTM enzyme regulation and function.     

An improved bimolecular fluorescence complementation assay with a high signal-to-noise ratio

Protein-protein interactions (PPIs) play crucial roles in various biological processes. Among biochemical, genetic, and imaging approaches that have been used for the study of PPIs, visualization of PPIs in living cells is the key to understanding their cellular functions. The bimolecular fluorescence complementation (BiFC) assay represents one of these imaging tools for direct visualization of PPIs in living cells. The BiFC assay is based on the structural complementation of two nonfluorescent N- and C-terminal fragments of a fluorescent protein when they are fused to a pair of interacting proteins. Although over 10 different fluorescent proteins have been used for BiFC assays, the two nonfluorescent fragments from all of these fluorescent proteins can spontaneously self-assemble, which contributes to background fluorescence and decreases the signal-to-noise (S/N) ratio in the BiFC assay. Here we report the identification of a mutation, I152L, that can specifically reduce self-assembly and decrease background fluorescence in a Venus-based BiFC system. This mutation allows a 4-fold increase in the S/N ratio of the BiFC assay in living cells. This improved Venus-based BiFC system will facilitate PPI studies in various biological research fields.     

Evolutionary and ecological functional genomics,from lab to the wild

Plant phenotypes are the result of both genetic and environmental forces that act to modulate trait expression. Over the last few years, numerous approaches in functional genomics and systems biology have led to a greater understanding of plant phenotypic variation and plant responses to the environment. These approaches, and the questions that they can address, have been loosely termed evolutionary and ecological functional genomics (EEFG), and have been providing key insights on how plants adapt and evolve. In particular, by bringing these studies from the laboratory to the field, EEFG studies allow us to gain greater knowledge of how plants function in their natural contexts.     

Green pathways: Metabolic network analysis of plant systems

Metabolic engineering of plants with enhanced crop yield and value-added compositional traits is particularly challenging as they probably exhibit the highest metabolic network complexity of all living organisms. Therefore, approaches of plant metabolic network analysis, which can provide systems-level understanding of plant physiology, appear valuable as guidance for plant metabolic engineers. Strongly supported by the sequencing of plant genomes, a number of different experimental and computational methods have emerged in recent years to study plant systems at various levels: from heterotrophic cell cultures to autotrophic entire plants. The present review presents a state-of-the-art toolbox for plant metabolic network analysis. Among the described approaches are different in silico modeling techniques, including flux balance analysis, elementary flux mode analysis and kinetic flux profiling, as well as different variants of experiments with plant systems which use radioactive and stable isotopes to determine in vivo plant metabolic fluxes. The fundamental principles of these techniques, the required data input and the obtained flux information are enriched by technical advices, specific to plants. In addition, pioneering and high-impacting findings of plant metabolic network analysis highlight the potential of the field.     

Radiofrequency and microwave interactions between biomolecular systems

The knowledge of mechanisms underlying interactions between biological systems, be they biomacromolecules or living cells, is crucial for understanding physiology, as well as for possible prevention, diagnostics and therapy of pathological states. Apart from known chemical and direct contact electrical signaling pathways, electromagnetic phenomena were proposed by some authors to mediate non-chemical interactions on both intracellular and intercellular levels. Here, we discuss perspectives in the research of nanoscale electromagnetic interactions between biosystems on radiofrequency and microwave wavelengths. Based on our analysis, the main perspectives are in (i) the micro and nanoscale characterization of both passive and active radiofrequency properties of biomacromolecules and cells, (ii) experimental determination of viscous damping of biomacromolecule structural vibrations and (iii) detailed analysis of energetic circumstances of electromagnetic interactions between oscillating polar biomacromolecules. Current cutting-edge nanotechnology and computational techniques start to enable such studies so we can expect new interesting insights into electromagnetic aspects of molecular biophysics of cell signaling.     

The levels of analysis revisited

The term levels of analysis has been used in several ways: to distinguish between ultimate and proximate levels, to categorize different kinds of research questions and to differentiate levels of reductionism. Because questions regarding ultimate function and proximate mechanisms are logically distinct, I suggest that distinguishing between these two levels is the best use of the term. Integrating across levels in research has potential risks, but many benefits. Consideration at one level can help generate novel hypotheses at the other, define categories of behaviour and set criteria that must be addressed. Taking an adaptationist stance thus strengthens research on proximate mechanisms. Similarly, it is critical for researchers studying adaptation and function to have detailed knowledge of proximate mechanisms that may constrain or modulate evolutionary processes. Despite the benefits of integrating across ultimate and proximate levels, failure to clearly identify levels of analysis, and whether or not hypotheses are exclusive alternatives, can create false debates. Such non-alternative hypotheses may occur between or within levels, and are not limited to integrative approaches. In this review, I survey different uses of the term levels of analysis and the benefits of integration, and highlight examples of false debate within and between levels. The best integrative biology reciprocally uses ultimate and proximate hypotheses to generate a more complete understanding of behaviour.     

Optical coherence tomography for dynamic investigation of mammalian reproductive processes

The biological events associated with mammalian reproductive processes are highly dynamic and tightly regulated by molecular, genetic, and biomechanical factors. Implementation of live imaging in reproductive research is vital for the advancement of our understanding of normal reproductive physiology and for improving the management of reproductive disorders. Optical coherence tomography (OCT) is emerging as a promising tool for dynamic volumetric imaging of various reproductive processes in mice and other animal models. In this review, we summarize recent studies employing OCT-based approaches toward the investigation of reproductive processes in both, males and females. We describe how OCT can be applied to study structural features of the male reproductive system and sperm transport through the male reproductive tract. We review OCT applications for in vitro and dynamic in vivo imaging of the female reproductive system, staging and tracking of oocytes and embryos, and investigations of the oocyte/embryo transport through the oviduct. We describe how the functional OCT approach can be applied to the analysis of cilia dynamics within the male and female reproductive systems. We also discuss the areas of research, where OCT could find potential applications to progress our understanding of normal reproductive physiology and reproductive disorders.     

Understanding biological functions through molecular networks

The completion of genome sequences and subsequent high-throughput mapping of molecular networks have allowed us to study biology from the network perspective. Experimental, statistical and mathematical modeling approaches have been employed to study the structure, function and dynamics of molecular networks, and begin to reveal important links of various network properties to the functions of the biological systems. In agreement with these functional links, evolutionary selection of a network is apparently based on the function, rather than directly on the structure of the network. Dynamic modularity is one of the prominent features of molecular networks. Taking advantage of such a feature may simplify network-based biological studies through construction of process-specific modular networks and provide functional and mechanistic insights linking genotypic variations to complex traits or diseases, which is likely to be a key approach in the next wave of understanding complex human diseases. With the development of ready-to-use network analysis and modeling tools the networks approaches will be infused into everyday biological research in the near future.     

Probability-based model of protein-protein interactions on biological timescales

Background  

Simulation methods can assist in describing and understanding complex networks of interacting proteins, providing fresh insights into the function and regulation of biological systems. Recent studies have investigated such processes by explicitly modelling the diffusion and interactions of individual molecules. In these approaches, two entities are considered to have interacted if they come within a set cutoff distance of each other.     

Levels or Domains of Life?

In the case of living beings – the very concept of “level” of organization becomes obscure: it suggests a value-based assessment, assigning notions like “lower” and “higher” with rather vague criteria for constructing the ladder of perfection, complexity, importance, etc. We prefer therefore the term “domain”, entities ranking equal. Domains may represent natural entities as well as purely human constructs developed in order to gain understanding of some facets of living things; living, evolved beings (e.g. viviparous animals, eukaryotic cells, etc.) as well as those abstract constructs, such as genotype and ‘niche’ which have been developed in the search for better understanding of such living things. Delimitation of such domains is sometimes a question of the dexterity of the researcher, and sometimes draws from the tradition in a given field. Such domains are not completely (canonically) translatable to each other. Rather, they interact by a process that we call here reciprocal formation. Life (including the biosphere and human cultures which are emergent within the frame of the biosphere) is unique among multi-domain systems. In contrast to purely physical systems, life is a semiotic system driven by the historical experience of lineages, interpreted and re-interpreted by the incessant turnover of both individuals and their communities. This paper provides cases of domain interrelations, and addresses two questions: (1) How do new qualities of inter-domain interaction emerge historically? (2) How do new domains (ways of understanding the world) emerge in evolution. Two approaches, physical and biosemiotic, are discussed as we seek to get a better understanding of the overarching tasks.     

From the ground up: Integrative research in primate locomotion

Primate locomotor adaptation and evolution is a principal and thriving area of research by biological anthropologists. Research in this field generally targets hypotheses regarding locomotor kinetics and kinematics, form–function associations in both the soft and hard tissue components of the musculoskeletal system, and reconstructing locomotor behavior in fossil primates. A wide array of methodological approaches is used to address adaptive hypotheses in all of these realms. Recent advances in three-dimensional shape capture, musculoskeletal physiological measurements, and analytical processing technologies (e.g., laser and CT-scans, 3D motion analysis systems, finite element analysis) have facilitated the collection and analysis of larger and more complex locomotor datasets than previously possible. With these advances in technology, new methods of form–function analyses can be developed to produce a more thorough understanding of how form reflects an organism's mechanical requirements, how shape is influenced by external environmental factors, and how these investigations of living taxa can inform questions of primate paleobiology. The papers in this special section of the American Journal of Physical Anthropology present research that builds on that foundation, by combining new data on living primates and new methodologies and approaches to answer a range of questions on extant and extinct primates. Am J Phys Anthropol 156:495–497, 2015. © 2015 Wiley Periodicals, Inc.     

RNA interference: past, present and future

RNA interference (RNAi) is the sequence-specific gene silencing induced by double-stranded RNA. RNAi is mediated by 21-23 nucleotide small interfering RNAs (siRNAs) which are produced from long double-stranded RNAs by RNAse II-like enzyme Dicer. The resulting siRNAs are incorporated into a RNA-induced silencing complex (RISC) that targets and cleaves mRNA complementary to the siRNAs. Since its inception in 1998, RNAi has been demonstrated in organisms ranging from trypanosomes to nematodes to vertebrates. Potential uses already in progress include the examination of specific gene function in living systems, the development of anti-viral and anti-cancer therapies, and genome-wide screens. In this review, we discuss the landmark discoveries that established the contextual framework leading up to our current understanding of RNAi. We also provide an overview of current developments and future applications.     

Integrating modelling of biodiversity composition and ecosystem function

There is increasing reliance on ecological models to improve our understanding of how ecological systems work, to project likely outcomes under alternative global change scenarios and to help develop robust management strategies. Two common types of spatiotemporally explicit ecological models are those focussed on biodiversity composition and those focussed on ecosystem function. These modelling disciplines are largely practiced separately, with separate literature, despite growing evidence that natural systems are shaped by the interaction of composition and function. Here we call for the development of new modelling approaches that integrate composition and function, accounting for the important interactions between these two dimensions, particularly under rapid global change. We examine existing modelling approaches that have begun to combine elements of composition and function, identifying their potential contribution to fully integrated modelling approaches. The development and application of integrated models of composition and function face a number of important challenges, including biological data limitations, system knowledge and computational constraints. We suggest a range of promising avenues that could help researchers overcome these challenges, including the use of virtual species, macroecological relationships and hybrid correlative‐mechanistic modelling. Explicitly accounting for the interactions between composition and function within integrated modelling approaches has the potential to improve our understanding of ecological systems, provide more accurate predictions of their future states and transform their management. Synthesis There is increasing attention from researchers and policy makers around the world on both assessing and projecting the state of the planet's biodiversity, its ecosystems and the essential services they provide to society. However, existing modelling approaches largely ignore the interactions between biodiversity composition and ecosystem function. We highlight the key challenges and potential solutions to developing integrated models of composition and function. Such models will require a new effort and focus from ecologists, yet the benefits are likely to be substantial, including better informing the management of natural systems at regional, national and international scales.     

Automated data integration for developmental biological research

In an era exploding with genome-scale data, a major challenge for developmental biologists is how to extract significant clues from these publicly available data to benefit our studies of individual genes, and how to use them to improve our understanding of development at a systems level. Several studies have successfully demonstrated new approaches to classic developmental questions by computationally integrating various genome-wide data sets. Such computational approaches have shown great potential for facilitating research: instead of testing 20,000 genes, researchers might test 200 to the same effect. We discuss the nature and state of this art as it applies to developmental research.     

Multivariate analysis in biological anthropology: Some considerations

The relation between modern multivariate statistical analysis and common objectives in biological anthropology is discussed. Many formal statistical assumptions should be satisfied to fully justify multi-variate analysis, but rarely are. Their failure does not have as adverse an effect upon scientific research as some procedural errors in research design. These include the treatment of single specimens as if they are samples, and the failure to separate considerations of size and shape. The superiority of elaborate multivariate distances over simpler quantitative techniques, inferred from the former's consideration of trait intercorrelations, is unwarranted from both theoretical and practical aspects. Based on these points and several comparative examples of analyses, it is argued that simpler, non-statistical approaches such as those of the school of numerical taxonomy have much to offer primato-logical studies. Classic multivariate statistics, while useful for studying closely similar living populations, are considerably more limited in usefulness and significance for paleoanthropological studies.     

Aqueous polymer two-phase systems: effective tools for plasma membrane proteomics

Plasma membranes (PMs) are of particular importance for all living cells. They form a selectively permeable barrier to the environment. Many essential tasks of PMs are carried out by their proteinaceous components, including molecular transport, cell-cell interactions, and signal transduction. Due to the key role of these proteins for cellular function, they take center-stage in basic and applied research. A major problem towards in-depth identification and characterization of PM proteins by modern proteomic approaches is their low abundance and immense heterogeneity in different cells. Highly selective and efficient purification protocols are hence essential to any PM proteome analysis. An effective tool for preparative isolation of PMs is partitioning in aqueous polymer two-phase systems. In two-phase systems, membranes are separated according to differences in surface properties rather than size and density. Despite their rare application to the fractionation of animal tissues and cells, they represent an attractive alternative to conventional fractionation protocols. Here, we review the principles of partitioning using aqueous polymer two-phase systems and compare aqueous polymer two-phase systems with other methods currently used for the isolation of PMs.