Maize centromeres: where sequence meets epigenetics

The centromere is a highly organized structure mainly composed of repeat sequences, which make this region extremely difficult for sequencing and other analyses. It plays a conserved role in equal division of chromosomes into daughter cells in both mitosis and meiosis. However, centromere sequences show notable plasticity. In a dicentric chromosome, one of the centromeres can become inactivated with the underlying DNA unchanged. Furthermore, formerly inactive centromeres can regain activity under certain conditions. In addition, neocentromeres without centromeric repeats have been found in a wide spectrum of species. This evidence indicates that epigenetic mechanisms together with centromeric sequences are associated with centromere specification.     

Somite formation: where left meets right

Somites are the bilaterally symmetric embryonic precursors of the vertebrate skeleton and axial muscle. Three recent studies reveal that somites form asymmetrically in the absence of retinoic acid signaling. These results uncover an unexpected relationship between somitogenesis and left-right patterning, and suggest that bilateral somite formation is regulated along the left-right axis.     

Plant transposable elements: where genetics meets genomics

Transposable elements are the single largest component of the genetic material of most eukaryotes. The recent availability of large quantities of genomic sequence has led to a shift from the genetic characterization of single elements to genome-wide analysis of enormous transposable-element populations. Nowhere is this shift more evident than in plants, in which transposable elements were first discovered and where they are still actively reshaping genomes.     

Quantitative biology: where modern biology meets physical sciences

Quantitative methods and approaches have been playing an increasingly important role in cell biology in recent years. They involve making accurate measurements to test a predefined hypothesis in order to compare experimental data with predictions generated by theoretical models, an approach that has benefited physicists for decades. Building quantitative models in experimental biology not only has led to discoveries of counterintuitive phenomena but has also opened up novel research directions. To make the biological sciences more quantitative, we believe a two-pronged approach needs to be taken. First, graduate training needs to be revamped to ensure biology students are adequately trained in physical and mathematical sciences and vice versa. Second, students of both the biological and the physical sciences need to be provided adequate opportunities for hands-on engagement with the methods and approaches necessary to be able to work at the intersection of the biological and physical sciences. We present the annual Physiology Course organized at the Marine Biological Laboratory (Woods Hole, MA) as a case study for a hands-on training program that gives young scientists the opportunity not only to acquire the tools of quantitative biology but also to develop the necessary thought processes that will enable them to bridge the gap between these disciplines.What does a mathematician looking at bacterial division under a microscope have in common with a biologist programming a stochastic simulation of microtubule growth? For one, both can be found at the Physiology Course at the Marine Biological Laboratory (MBL) in Woods Hole, MA, which brings together graduate students and young postdocs who have a passion for quantitative biology. Students enter the course with a wide array of scientific backgrounds, including chemistry, molecular biology, mathematics, and theoretical physics. Although at first hesitant to step outside their comfort zones, students leave the course confident and courageous in their abilities to work across traditional academic boundaries. Having experienced this transformation ourselves as participants in the 2014 Physiology Course, we wanted to share some of our insights and how they have influenced our perspectives on the present challenges and exciting future of quantitative cell biology.“When you cannot express [what you are speaking about] in numbers, your knowledge is of a meager and unsatisfactory kind; it may be the beginning of knowledge, but you have scarcely, in your thoughts, advanced to the stage of science.” The need for quantification in the life sciences could not have been better worded than it is in this quote from Lord Kelvin. One of the key take-home messages from the course has been the crucial need for advancement of quantitative cell biology, which uses accurate measurements to refine a hypothesis, with the aim of comparing experimental data with predictions generated by theoretical models. We strongly believe that quantitative approaches not only aid in better addressing existing biological questions but also enable the formulation of new ones.The present time is particularly ripe for implementing quantitative approaches in cell biology, due to the wealth of data available and the depth of control we now have over many experimental systems. In the past 20 years, we have sequenced the human genome, broken the diffraction limit in microscopy, and begun to explore the possibilities of the micron-scaled experiments with microfluidics. With these tools in hand, the means to obtain quantitative data are not limited to a select few model systems; this level of experimental detail allows us to craft theoretical models that not only fit the data but have real predictive power. We can then return to our respective experimental systems with new hypotheses and interrogate them anew, reaping the benefits of an approach that has benefited physicists for decades.Building quantitative models in biology has been a powerful approach that has often revealed counterintuitive phenomena and insights while at the same time leading to novel research directions. This is of particular importance today, as experiments are becoming increasingly expensive and are rapidly accumulating vast amounts of data. It is now possible to perform “virtual” preliminary experiments in silico using quantitative models and pre-existing data and only then move to “real” laboratory experiments to test the developed hypotheses. Researchers trained this way can perform more focused experiments instead of adopting the traditional exploratory mode in the lab, saving both time and resources. However, we recognize that a majority of biology graduates have not been rigorously trained in the mathematical and physical sciences. Similarly, many physics graduates often remember their introductory biology classes simply for the rote memorization of protein names and signaling pathways, leading to the wrong assumption that biology is all about remembering three-letter abbreviations such as WNT, MYC, and so on. This can often create a misleading picture of biology.These challenges could be overcome by finding a common language between biologists, physicists, and mathematicians. A simple example of this is the word “model.” The same word can mean very different things to scientists depending upon their training: to a physicist it refers to quantitative visualization of a process via certain well-defined mathematical parameters; a biologist, on the other hand, might use the word to refer to a schematic depiction (also called a cartoon) of a biochemical reaction. We aim to reduce this gap between biological and physical sciences and bring these two communities together.One way to train young scientists in such an approach is to provide opportunities for hands-on engagement with the methods and thought processes necessary to partake in both fields. The MBL Physiology Course is an excellent case study for a training program that gives young scientists the building blocks and community necessary for success in bridging quantitative/physical sciences and biology. The course starts with a weeklong boot camp designed to bring students of different backgrounds up to speed on basic tools in quantitative biology. Students purify proteins, program in MATLAB, and build microscopes. The most important skill that biologists acquire is not simply learning how to write lines of MATLAB code, but rather phrasing biological phenomena in mathematical terms through equations and simulations. Building confocal microscopes and optical tweezers on a bare optical table creates trust in the tools we depend on to acquire quantitative data. Physicists, on the other hand, learn to purify motor proteins like kinesins and dyneins from native sources (squid), in the process coming face-to-face with the natural context of the biological questions that they are addressing.This interdisciplinary approach helps students from diverse backgrounds develop a common language. After the boot camp, students work together on three 2-week-long research projects under the guidance of leading scientists. Projects range from studying the spatial organization of the human oral microbiome and observing the development of the Caenorhabditis elegans embryo all the way to performing computational simulations of cytoskeletal polymers. By working together in a highly informal and stimulating environment, physicists learn to appreciate biological problems and biologists begin to see biological phenomena in a new light as a result of the novel physical tools and methodologies they learn from their peers. As an example, course participants Rikki Garner and Daniel Feliciano successfully collaborated to study how competition between two highly processive microtubule motors that work in opposition controls microtubule length. While Rikki (mentored by Jané Kondev) tackled the question theoretically using a random walk model, Daniel, under the mentorship of Joe Howard, carried out the experimental measurements via an in vitro assay to test Rikki''s predictions. Other examples of quantitative and biological expertise coming together to address biological questions include studying the displacement and transport of proteins at the interface between cells and synthetic supported lipid bilayers (Figure 1), observing and quantifying the cytoplasmic streaming as well as the filter-feeding flow vortices in the giant single-celled organism Stentor coeruleus (Figure 2), and imaging the spatial organization of complex oral microbial communities (Figure 3).Open in a separate windowFIGURE 1:Proteins are organized based on size at the membrane interface. The membrane interface between a cell and a supported lipid bilayer (SLB) was formed by the interaction of synthetic adhesion molecules, one protein (bound to the membrane via a His-tag) and another protein that interacts and binds with the membrane-bound protein (expressed in the S2 cells). Note that the long noninteracting protein (21 nm, magenta colored) but not the shorter noninteracting protein (8 nm, magenta colored), which is bigger than the synthetic interacting dimer (16 nm, red–green colored) is excluded from the cell–SLB interface. Both of these noninteracting proteins are bound to the SLB and do not interact with the cell. Scale bar: 10 μm. (Prepared by L.Z. and Nan Hyung Hong under the guidance of Matt Bakalar, Eva Schmid, and Dan Fletcher.)Open in a separate windowFIGURE 2:Stentor coeruleus is a giant single-celled organism that feeds by creating flow vortices in water and directing prey into its oral opening using this flow. (A) Maximum intensity projection of time-lapse images showing flow fields in the feeding flow generated by S. coeruleus. The flow is generated by the coordinated ciliary beating of the mouth cilia. (B) Flow velocity and flow directions were quantified by the particle image velocimetry method. The circularity of flow has also been indicated—the blue cloud around the oral cilia indicates clockwise flow, and red indicates anticlockwise flow. Scale bar: 100 μm. (Prepared by S.S. under the guidance of Mark Slabodnick, Tatyana Makushok, and Wallace Marshall in collaboration with Jack Costello, Providence College.)Open in a separate windowFIGURE 3:Spatial organization of complex microbial communities in an oral plaque sample taken from a volunteer as seen by combinatorial labeling and spectral imaging–fluorescent in situ hybridization (CLASI-FISH). Microbes seen here are Corynebacterium (pink), Neisseriaceae (blue), Fusobacterium (green), Pasteurellaceae (yellow), Streptococcus (cyan), and Actinomyces (red). (Prepared by Bryan Weinstein, Lishibanya Mohapatra, and Matti Gralka under the guidance of Blair Rossetti, Jessica Mark Welch, and Gary Borisy.)An invaluable aspect of the course is the informal nature of the interaction. There are a wide variety of morning seminar speakers, and in the question-and-answer sessions following the talks, the speakers discuss not only science but also the successes and failures they experienced while moving across the boundaries of biological and physical sciences. The interactive and collaborative nature of the course encourages students to not just learn from one another but to actually teach one another. “Chalk talks” and interactions happen spontaneously and are the strongest indication of the richness of the intellectual exchange among members of the community. Prime examples in the 2014 course are the chalk talks on Python (Bryan Weinstein), Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) cloning (Dan Dickinson), and the basics of microfluidics (Sindy Tang).Although we have benefited immensely from this interdisciplinary course, we understand that it might not be feasible for all graduate students to participate in such courses. Nevertheless, we believe that the scientific community should work together to replicate elsewhere, at least partially, the strengths of this course to allow students to benefit from this approach. Students should be encouraged to host and attend seminars from speakers with diverse backgrounds, which will expose them to research areas different from their own. Biology students should also be exposed to mathematical and statistical instruction early in their research careers, preferably at the undergraduate level, to enable them to build strong foundations. Students should also be encouraged to participate in short-term, low-pressure interdisciplinary collaborations to broaden their understanding and initiate interactions with other fields. Short summer/winter schools—for example, the physical biology of the cell courses at Cold Spring Harbor and the International Centre for Theoretical Physics (Italy)–International Centre for Theoretical Sciences (India) Winter School on Quantitative Systems Biology, 2013, in Bangalore, India—can serve as the perfect stage for this.In conclusion, we expect that quantitative approaches will be indispensable for better addressing biological questions in the future. Our experience is that combining traditional experimental cell biology with quantitative thinking leads to hitherto unknown scientifically rich domains, and we ourselves have found this exploratory journey to be both achievable and rewarding. Although bridging the gap may appear to be difficult at times, it is extremely satisfying when accomplished, and doing it within a highly motivated and supportive community is what makes the connection possible and extremely useful.     

Retinoblastoma tumor suppressor: where cancer meets the cell cycle

The retinoblastoma tumor suppressor gene, Rb, was the first tumor suppressor identified and plays a fundamental role in regulation of progression through the cell cycle. This review details facets of RB protein function in cell cycle control and focuses on specific questions that remain intensive areas of investigation.     

Attractive amplifiers in sexual selection: where efficacy meets honesty

How signals have evolved to convey reliable information is a major issue in the study of animal communication. The handicap principle states that recipients must impose differential costs on signallers. Here, we present an alternative hypothesis, the attractive amplifier hypothesis, according to which recipients acquire reliable information by imposing differential benefits on signallers. The attractive amplifier is a non-informative, low-cost phenotypic trait that allows recipients to increase the amount of reliable information acquired from other informative traits. We present a mating-decision model, in which mate attractiveness depends on the multiplicative-interaction between a trait that is positively correlated with mate quality and its attractive amplifier. The evaluation of both traits is assumed to be prone to error. The model shows that, to be an amplifier of cues, an attractive trait must show two well-known consequences of signal ritualization: it must be conspicuous and it must be perceived as a highly stereotyped trait, that is, it must show a low variation at both the within-individual and the within-population levels.     

Hydrogen bonds in protein-DNA complexes: where geometry meets plasticity

Recognition of a DNA sequence by a protein is achieved by interface-coupled chemical and shape complementation. This complementation between the two molecules is clearly directional and is determined by the specific chemical contacts including mainly hydrogen bonds. Directionality is an instrumental property of hydrogen bonding as it influences molecular conformations, which also affects DNA-protein recognition. The prominent elements in the recognition of a particular DNA sequence by a protein are the hydrogen-bond donors and acceptors of the base pairs into the grooves of the DNA that must interact with complementary moieties of the protein partner. Protein side chains make most of the crucial contacts through bidentate and complex hydrogen-bonding interactions with DNA base edges hence conferring remarkable specificity.     

Developmental phenotypic plasticity: where internal programming meets the external environment

Developmental plasticity has long been the focus of research in both evolutionary ecology and molecular genetics. Recently, the concept of ontogenetic contingency has been proposed to indicate the dependence of plastic responses on the timing and sequence of developmental events. Also, the idea of the developmental reaction norm has been put forward to indicate the complex interactions among development, phenotypic plasticity, and allometry of different structures. Finally, for the first time, studies ranging from the ecological to the molecular aspects of the same plastic response are available on insect and flowering plant model systems.     

From Notch signaling to fine-grained patterning: Modeling meets experiments

Notch signaling is the canonical signaling pathway between neighboring cells. It plays an important role in fine-grained patterning processes such as the formation of checkerboard-like differentiation patterns and sharp boundaries between developing tissues. While detailed information about many of the genes and proteins involved have been identified, we still lack a quantitative mechanistic understanding of these processes. Here we discuss several recent studies that provide novel insights into Notch-dependent patterning by combining mathematical models with quantitative experimental results. Such approaches allow identification of mechanisms and design principles controlling how patterns are generated in a reproducible and robust manner.     

Ecological networks: information theory meets Darwin's entangled bank

Is it possible to untangle the 'entangled bank'--Darwin's metaphor for the complexity and connectedness of species in the natural world? Studies on webs of species interactions suggest so, but a major question remains unanswered: how specialized are different ecological networks? By considering how strongly species interact with each other, information theory may give the answer.     

Apis mellifera evolutionary lineages in Northern Africa: Libya, where orient meets occident

Abstract  The distribution of various evolutionary lineages of Apis mellifera subspecies in Africa is still controversial. We sampled honeybees from eight coastal locations and three Saharan oases in Libya and analyzed mtDNA variability with restriction fragment length polymorphisms and the sequence of the tRNAleu-cox2 intergenic region. Haplotypes belonging to the oriental O evolutionary lineage, including four which are newly described, were detected in all investigated locations. Haplotypes belonging to the European M lineage were rarely detected, probably reflecting the effect of sporadic importations. Honeybees belonging to the A lineage were detected in Al Aziziyah and Zlitan close to the Tunisian border. The distribution of the O lineage extends westward up to the border between Libya and Tunisia, a contact area between the O and A lineages. Various Libyan honeybee populations in Saharan oases are characterized by novel and unique haplotypes (O4, O5, O5′ and O5″). These might be natural relic populations that became isolated when the North African Sahara desert was still grassland (0.126–0.168 Myr ago).     

Organic matter quality in ecological studies: theory meets experiment

Despite its importance for the understanding of element cycles in ecosystems, organic matter (OM) quality has remained an elusive property that is difficult to measure. In this study, two new approaches, both of which taking into account the complete biochemical composition of the organic material during the decomposition process, have been combined to solve this problem. First, following the continuous-quality theory where quality is defined as a measure of substrate availability to the decomposers, initial litter OM qualities of a range of plant species from two experiments on litter decomposition were estimated and resulted in highly accurate fits of observed mass loss during decomposition. Applying the same theory, qualities of the litters at all stages of decomposition were then calculated. By comparison, the initial qualities of the same litters were estimated from conventional chemical fractions and resulted in much lower accurate fits. Second, near-infrared reflectance spectroscopy (NIRS), a highly precise physical method of characterising biochemical composition of OM, was used to obtain a unique spectral signature of each sample. Calibrations were performed between spectral data and calculated qualities on the first half of the sample set and the calibration equations were applied to the second half of the sample set. Results show that theoretical litter OM quality can be calibrated and predicted precisely using NIRS. OM quality, defined according to a theoretical concept of substrate availability to decomposers, then contains and summarises all the relevant biochemical information. We demonstrate how the combination of NIRS and theory allows us to accurately measure OM quality. Measurement of OM quality provides an access to a fundamental property of organic matter and opens up new possibilities for studying element cycles in ecosystems.     

Artificial grammar learning meets formal language theory: an overview

Formal language theory (FLT), part of the broader mathematical theory of computation, provides a systematic terminology and set of conventions for describing rules and the structures they generate, along with a rich body of discoveries and theorems concerning generative rule systems. Despite its name, FLT is not limited to human language, but is equally applicable to computer programs, music, visual patterns, animal vocalizations, RNA structure and even dance. In the last decade, this theory has been profitably used to frame hypotheses and to design brain imaging and animal-learning experiments, mostly using the 'artificial grammar-learning' paradigm. We offer a brief, non-technical introduction to FLT and then a more detailed analysis of empirical research based on this theory. We suggest that progress has been hampered by a pervasive conflation of distinct issues, including hierarchy, dependency, complexity and recursion. We offer clarifications of several relevant hypotheses and the experimental designs necessary to test them. We finally review the recent brain imaging literature, using formal languages, identifying areas of convergence and outstanding debates. We conclude that FLT has much to offer scientists who are interested in rigorous empirical investigations of human cognition from a neuroscientific and comparative perspective.     

ProDy: protein dynamics inferred from theory and experiments

SUMMARY: We developed a Python package, ProDy, for structure-based analysis of protein dynamics. ProDy allows for quantitative characterization of structural variations in heterogeneous datasets of structures experimentally resolved for a given biomolecular system, and for comparison of these variations with the theoretically predicted equilibrium dynamics. Datasets include structural ensembles for a given family or subfamily of proteins, their mutants and sequence homologues, in the presence/absence of their substrates, ligands or inhibitors. Numerous helper functions enable comparative analysis of experimental and theoretical data, and visualization of the principal changes in conformations that are accessible in different functional states. ProDy application programming interface (API) has been designed so that users can easily extend the software and implement new methods. AVAILABILITY: ProDy is open source and freely available under GNU General Public License from http://www.csb.pitt.edu/ProDy/.     

Tumour angiogenesis: the gap between theory and experiments

A common experimental technique for viewing in vivo angiogenesis utilises tumours implanted into a test animal cornea. The cornea is avascular but the tumour promotes vascularisation from the limbus and the new blood vessels can be readily observed through the transparent cornea. Many of the early mathematical models for tumour angiogenesis used this scenario as their experimental template and as such assumed that there is a large gap, of the order of 2 mm, between the tumour and neighbouring vasculature at the onset of angiogenesis. In this work we consider whether the assumption that there is a significant gap between the tumour and neighbouring vasculature is unique to intra-cornea tumour implants, or whether this characterises avascular tumour growth more generally. To do this we utilise a simple scaling argument, derive a multi-compartment model for tumour growth, and consider in vivo images. This analysis demonstrates that the corneal implant experiments and the corresponding mathematical models cannot generally be applied to a clinical setting.