Our understanding of the development of the retina and visual pathways has seen enormous advances during the past twenty-five years. New imaging technologies, coupled with advances in molecular biology, have permitted a fuller appreciation of the histotypical events associated with proliferation, fate determination, migration, differentiation, pathway navigation, target innervation, synaptogenesis and cell death, and in many instances, in understanding the genetic, molecular, cellular and activity-dependent mechanisms underlying those developmental changes. The present review considers those advances associated with the lineal relationships between retinal nerve cells, the production of retinal nerve cell diversity, the migration, patterning and differentiation of different types of retinal nerve cells, the determinants of the decussation pattern at the optic chiasm, the formation of the retinotopic map, and the establishment of ocular domains within the thalamus.
proliferation; fate determination; migration; differentiation; stratification; mosaic; coverage; axonal outgrowth; decussation; retinotopic map; binocular segregation
This commentary discusses the evolving sociocultural roles and sociocultural authority of chiropractic.
The complex interconnectivity of the biological, psychological, and social aspects of our individual and collective well-being has occupied centuries of “nature versus nurture” philosophical debate, creative art, and scientific work. What has emerged is a better understanding of how our human development is affected by the circumstances of what we are born with (ie, nature) and how we are shaped by the circumstances that we are born into (ie, nurture).
In the new millennium, a cumulative challenge to the emerging integrative biopsychosocial health care disciplines is one of reconciling “circumstance versus choice”; that is, advancing individually and collectively the fullest actualization of human potential through the philosophy, art, and science of autonomy and empowerment.
Chiropractic; Health care provider; Professional role
Motivation: Primary purpose of modeling gene regulatory networks for developmental process is to reveal pathways governing the cellular differentiation to specific phenotypes. Knowledge of differentiation network will enable generation of desired cell fates by careful alteration of the governing network by adequate manipulation of cellular environment.
Results: We have developed a novel integer programming-based approach to reconstruct the underlying regulatory architecture of differentiating embryonic stem cells from discrete temporal gene expression data. The network reconstruction problem is formulated using inherent features of biological networks: (i) that of cascade architecture which enables treatment of the entire complex network as a set of interconnected modules and (ii) that of sparsity of interconnection between the transcription factors. The developed framework is applied to the system of embryonic stem cells differentiating towards pancreatic lineage. Experimentally determined expression profile dynamics of relevant transcription factors serve as the input to the network identification algorithm. The developed formulation accurately captures many of the known regulatory modes involved in pancreatic differentiation. The predictive capacity of the model is tested by simulating an in silico potential pathway of subsequent differentiation. The predicted pathway is experimentally verified by concurrent differentiation experiments. Experimental results agree well with model predictions, thereby illustrating the predictive accuracy of the proposed algorithm.
Supplementary information: Supplementary data are available at Bioinformatics online.
The mammalian retina is a valuable model system to study neuronal biology in health and disease. To obtain insight into intrinsic processes of the retina, great efforts are directed towards the identification and characterization of transcripts with functional relevance to this tissue.
With the goal to assemble a first genome-wide reference transcriptome of the adult mammalian retina, referred to as the retinome, we have extracted 13,037 non-redundant annotated genes from nearly 500,000 published datasets on redundant retina/retinal pigment epithelium (RPE) transcripts. The data were generated from 27 independent studies employing a wide range of molecular and biocomputational approaches. Comparison to known retina-/RPE-specific pathways and established retinal gene networks suggest that the reference retinome may represent up to 90% of the retinal transcripts. We show that the distribution of retinal genes along the chromosomes is not random but exhibits a higher order organization closely following the previously observed clustering of genes with increased expression.
The genome wide retinome map offers a rational basis for selecting suggestive candidate genes for hereditary as well as complex retinal diseases facilitating elaborate studies into normal and pathological pathways. To make this unique resource freely available we have built a database providing a query interface to the reference retinome .
Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to discover whether there are rules or regularities governing development and evolution of complex multi-cellular organisms.
To better understand multi-cellular organisms we need a better and more systematic understanding of the complex regulatory networks that govern their development and evolution. However, this problem is far from trivial. Regulatory networks involve many factors interacting in a non-linear manner, which makes it difficult to study them without the help of computers. Here, we investigate a computational method, reverse engineering, which allows us to reconstitute real-world regulatory networks in silico. As a case study, we investigate the gap gene network involved in determining the position of body segments during early development of Drosophila. We visualise spatial gap gene expression patterns using in situ hybridisation and microscopy. The resulting embryo images are quantified to measure the position of expression domain boundaries. We then use computational models as tools to extract regulatory information from the data. We investigate what kind, and how much data are required for successful network inference. Our results reveal that much less effort is required for reverse-engineering networks than previously thought. This opens the possibility of investigating a large number of developmental networks using this approach, which in turn will lead to a more general understanding of the rules and principles underlying development in animals and plants.
Cellular constituents such as proteins, DNA, and RNA form a complex web of interactions that regulate biochemical homeostasis and determine the dynamic cellular response to external stimuli. It follows that detailed understanding of these patterns is critical for the assessment of fundamental processes in cell biology and pathology. Representation and analysis of cellular constituents through network principles is a promising and popular analytical avenue towards a deeper understanding of molecular mechanisms in a system-wide context.
We present Functional Genomics Assistant (FUGA) - an extensible and portable MATLAB toolbox for the inference of biological relationships, graph topology analysis, random network simulation, network clustering, and functional enrichment statistics. In contrast to conventional differential expression analysis of individual genes, FUGA offers a framework for the study of system-wide properties of biological networks and highlights putative molecular targets using concepts of systems biology.
FUGA offers a simple and customizable framework for network analysis in a variety of systems biology applications. It is freely available for individual or academic use at http://code.google.com/p/fuga.
The process of cellular differentiation is governed by complex dynamical biomolecular networks consisting of a multitude of genes and their products acting in concert to determine a particular cell fate. Thus, a systems level view is necessary for understanding how a cell coordinates this process and for developing effective therapeutic strategies to treat diseases, such as cancer, in which differentiation plays a significant role. Theoretical considerations and recent experimental evidence support the view that cell fates are high dimensional attractor states of the underlying molecular networks. The temporal behavior of the network states progressing toward different cell fate attractors has the potential to elucidate the underlying molecular mechanisms governing differentiation.
Using the HL60 multipotent promyelocytic leukemia cell line, we performed experiments that ultimately led to two different cell fate attractors by two treatments of varying dosage and duration of the differentiation agent all-trans-retinoic acid (ATRA). The dosage and duration combinations of the two treatments were chosen by means of flow cytometric measurements of CD11b, a well-known early differentiation marker, such that they generated two intermediate populations that were poised at the apparently same stage of differentiation. However, the population of one treatment proceeded toward the terminally differentiated neutrophil attractor while that of the other treatment reverted back toward the undifferentiated promyelocytic attractor. We monitored the gene expression changes in the two populations after their respective treatments over a period of five days and identified a set of genes that diverged in their expression, a subset of which promotes neutrophil differentiation while the other represses cell cycle progression. By employing promoter based transcription factor binding site analysis, we found enrichment in the set of divergent genes, of transcription factors functionally linked to tumor progression, cell cycle, and development.
Since many of the transcription factors identified by this approach are also known to be implicated in hematopoietic differentiation and leukemia, this study points to the utility of incorporating a dynamical systems level view into a computational analysis framework for elucidating transcriptional mechanisms regulating differentiation.
A fundamental question in developmental neuroscience is how a collection of progenitor cells proliferates and differentiates to create a brain of the appropriate size and cellular composition. To address this issue, we devised lineage-tracing assays in developing zebrafish embryos to reconstruct entire retinal lineage progressions in vivo and thereby provide a complete quantitative map of the generation of a vertebrate CNS tissue from individual progenitors. These lineage data are consistent with a simple model in which the retina is derived from a set of equipotent retinal progenitor cells (RPCs) that are subject to stochastic factors controlling lineage progression. Clone formation in mutant embryos reveals that the transcription factor Ath5 acts as a molecular link between fate choice and mode of cell division, giving insight into the elusive molecular mechanisms of histogenesis, the conserved temporal order by which neurons of different types exit the cell cycle.
► Method for full live lineage tracing of retinal cells in vivo ► Demonstration that retinal clone growth is representative of retinal growth ► A stochastic model accurately predicts clone growth from equipotent progenitors ► Links between mode of cell division and cell fate help explain histogenesis
A key question in developmental neuroscience is how a collection of progenitors proliferates and differentiates to create a brain of the consistent size and composition. He et al. use lineage tracing to reconstruct the full retinal lineages in vivo and propose a model for stochastic control of lineage progression.
In the developing nervous system, building a functional neuronal network relies on coordinating the formation, specification and survival to diverse neuronal and glial cell subtypes. The establishment of neuronal connections further depends on sequential neuron–neuron and neuron–glia interactions that regulate cell-migration patterns and axon guidance. The visual system of Drosophila has a highly regular, retinotopic organization into reiterated interconnected synaptic circuits. It is therefore an excellent invertebrate model to investigate basic cellular strategies and molecular determinants regulating the different developmental processes that lead to network formation. Studies in the visual system have provided important insights into the mechanisms by which photoreceptor axons connect with their synaptic partners within the optic lobe. In this review, we highlight that this system is also well suited for uncovering general principles that underlie glial cell biology. We describe the glial cell subtypes in the visual system and discuss recent findings about their development and migration. Finally, we outline the pivotal roles of glial cells in mediating neural circuit assembly, boundary formation, neural proliferation and survival, as well as synaptic function.
Optic lobe; photoreceptor axons; axon guidance; migration; neurogenesis
Highly complex molecular networks, which play fundamental roles in almost all cellular processes, are known to be dysregulated in a number of diseases, most notably in cancer. As a consequence, there is a critical need to develop practical methodologies for constructing and analysing molecular networks at a systems level. Mathematical models built with continuous differential equations are an ideal methodology because they can provide a detailed picture of a network’s dynamics. To be predictive, however, differential equation models require that numerous parameters be known a priori and this information is almost never available. An alternative dynamical approach is the use of discrete logic-based models that can provide a good approximation of the qualitative behaviour of a biochemical system without the burden of a large parameter space. Despite their advantages, there remains significant resistance to the use of logic-based models in biology. Here, we address some common concerns and provide a brief tutorial on the use of logic-based models, which we motivate with biological examples.
Understanding the gene networks that orchestrate the differentiation of retinal progenitors into photoreceptors in the developing retina is important not only due to its therapeutic applications in treating retinal degeneration but also because the developing retina provides an excellent model for studying CNS development. Although several studies have profiled changes in gene expression during normal retinal development, these studies offer at best only a starting point for functional studies focused on a smaller subset of genes. The large number of genes profiled at comparatively few time points makes it extremely difficult to reliably infer gene networks from a gene expression dataset. We describe a novel approach to identify and prioritize from multiple gene expression datasets, a small subset of the genes that are likely to be good candidates for further experimental investigation. We report progress on addressing this problem using a novel approach to querying multiple large-scale expression datasets using a ‘seed network’ consisting of a small set of genes that are implicated by published studies in rod photoreceptor differentiation. We use the seed network to identify and sort a list of genes whose expression levels are highly correlated with those of multiple seed network genes in at least two of the five gene expression datasets. The fact that several of the genes in this list have been demonstrated, through experimental studies reported in the literature, to be important in rod photoreceptor function provides support for the utility of this approach in prioritizing experimental targets for further experimental investigation. Based on Gene Ontology and KEGG pathway annotations for the list of genes obtained in the context of other information available in the literature, we identified seven genes or groups of genes for possible inclusion in the gene network involved in differentiation of retinal progenitor cells into rod photoreceptors. Our approach to querying multiple gene expression datasets using a seed network constructed from known interactions between specific genes of interest provides a promising strategy for focusing hypothesis-driven experiments using large-scale ‘omics’ data.
gene expression; gene network; cell fate determination; retina; photoreceptor
Retinal development occurs in mice between embryonic day E11.5 and post-natal day P8 as uncommitted neuroblasts assume retinal cell fates. The genetic pathways regulating retinal development are being identified but little is understood about the global networks that link these pathways together or the complexity of the expressed gene set required to form the retina. At E14.5, the retina contains mostly uncommitted neuroblasts and newly differentiated neurons. Here we report a sequence analysis of an E14.5 retinal cDNA library. To date, we have archived 15 268 ESTs and have annotated 9035, which represent 5288 genes. The fraction of singly occurring ESTs as a function of total EST accrual suggests that the total number of expressed genes in the library could approach 27 000. The 9035 ESTs were categorized by their known or putative functions. Representation of the genes involved in eye development was significantly higher in the retinal clone set compared with the NIA mouse 15K cDNA clone set. Screening with a microarray containing 864 cDNA clones using wild-type and brn-3b (–/–) retinal cDNA probes revealed a potential regulatory linkage between the transcription factor Brn-3b and expression of GAP-43, a protein associated with axon growth. The retinal EST database will be a valuable platform for gene expression profiling and a new source for gene discovery.
Iron is required for survival of mammalian cells. Recently, understanding of iron metabolism and trafficking has increased dramatically, revealing a complex, interacting network largely unknown just a few years ago. This provides an excellent model for systems biology development and analysis. The first step in such an analysis is the construction of a structural network of iron metabolism, which we present here. This network was created using CellDesigner version 3.5.2 and includes reactions occurring in mammalian cells of numerous tissue types. The iron metabolic network contains 151 chemical species and 107 reactions and transport steps. Starting from this general model, we construct iron networks for specific tissues and cells that are fundamental to maintaining body iron homeostasis. We include subnetworks for cells of the intestine and liver, tissues important in iron uptake and storage, respectively; as well as the reticulocyte and macrophage, key cells in iron utilization and recycling. The addition of kinetic information to our structural network will permit the simulation of iron metabolism in different tissues as well as in health and disease.
iron; liver; macrophage; reactive oxygen species; red blood cells
The low-density lipoprotein receptor-related protein 5 (LRP5) plays an important role in the development of retinal vasculature. LRP5 loss-of-function mutations cause incomplete development of retinal vessel network in humans as well as in mice. To understand the underlying mechanism for how LRP5 mutations lead to retinal vascular abnormalities, we have determined the retinal cell types that express LRP5 and investigated specific molecular and cellular functions that may be regulated by LRP5 signaling in the retina.
Methods and Findings
We characterized the development of retinal vasculature in LRP5 mutant mice using specific retinal cell makers and a GFP transgene expressed in retinal endothelial cells. Our data revealed that retinal vascular endothelial cells predominantly formed cell clusters in the inner-plexiform layer of LRP5 mutant retina rather than sprouting out or migrating into deeper layers to form normal vascular network in the retina. The IRES-β-galactosidase (LacZ) report gene under the control of the endogenous LRP5 promoter was highly expressed in Müller cells and was also weakly detected in endothelial cells of the retinal surface vasculature. Moreover, the LRP5 mutant mice had a reduction of a Müller cell-specific glutamine transporter, Slc38a5, and showed a decrease in b-wave amplitude of electroretinogram.
LRP5 is not only essential for vascular endothelial cells to sprout, migrate and/or anastomose in the deeper plexus during retinal vasculature development but is also important for the functions of Müller cells and retinal interneurons. Müller cells may utilize LRP5-mediated signaling pathway to regulate vascular development in deeper layers and to maintain the function of retinal interneurons.
The generation of complex organ structures such as the eye requires the intricate orchestration of multiple cellular interactions. In this paper, early retinal development is discussed with respect to the structure formation of the optic cup. Although recent studies have elucidated molecular mechanisms of retinal differentiation, little is known about how the unique shape of the optic cup is determined. A recent report has demonstrated that optic-cup morphogenesis spontaneously occurs in three-dimensional stem-cell culture without external forces, indicating a latent intrinsic order to generate the structure. Based on this self-organizing phenomenon, we introduce the “relaxation-expansion” model to mechanically interpret the tissue dynamics that enable the spontaneous invagination of the neural retina. This model involves three consecutive local rules (relaxation, apical constriction, and expansion), and its computer simulation recapitulates the optic-cup morphogenesis in silico.
ES cells; internal force; optic cup; retina; self-organization
Asymmetric cell division (ACD) is the fundamental mechanism underlying the generation of cellular diversity in invertebrates and vertebrates. During Drosophila neuroblast division, this process involves stabilization of the apical complex and interaction between the Inscuteable (Insc) and Partner of inscuteable (Pins) proteins. Both cell-intrinsic factors and cell–cell interactions seem to contribute to cell fate decisions in the retina. The Pins protein is known to play a major role in the asymmetric segregation of cell fate determinants during development of the central nervous system in general, but its role in asymmetric cell divisions and retinoblast cell fate has never been explored. The primary aim of this study was to determine the spatial distribution and time course of mouse homolog of Drosophila Partner of Inscuteable (mPins) expression in the developing and adult mouse eye.
The expression pattern of mPins was studied in the mouse eye from embryonic (E) stage E11.5 until adulthood, by semiquantitative RT–PCR, in situ hybridization, and immunohistochemistry. In addition, variations in mRNA and protein levels for mPins were analyzed in the developing postnatal and adult lens, by semiquantitative RT–PCR, western blot analysis, in situ hybridization, and immunohistochemistry.
We detected mPins mRNA at early stages of mouse embryonic eye development, particularly in the neuroblastic layer. In early postnatal development, mPins mRNA was still detected in the neuroblastic layer, but also began to be detectable in the ganglion cell layer. Thereafter, mPins mRNA was found throughout the retina. This pattern was maintained in differentiated adult retina. Immunohistochemical studies showed that mPins protein was present in the neuroblastic layer and the ganglion cell layer during the early stages of postnatal retinal development. At these stages, mPins protein was colocalized with Numb protein, a marker of the ACD. At later postnatal stages, mPins protein was present in all retinal nuclear layers and in the inner plexiform layer. It continued to be detected in these layers in the differentiated retina; the outer plexiform layer and the photoreceptor inner segments also began to display positive immunostaining for mPins. In the adult retina, mPins was also detected in the retinal pigment epithelium and choroidal melanocytes. Throughout development, mPins protein was detected in nonretinal tissues, including the cornea, ciliary body, and lens. We focused our attention on lens development and showed that mPins protein was first detected at E14.5. The most striking results obtained concerned the lens, in which mPins protein distribution switched from the anterior to the posterior region of the lens during embryonic development. Interestingly, in the postnatal and adult lens, mPins protein was detected in all lens cells and fibers.
We provide the first demonstration that mPins protein is expressed from embryonic stages until adulthood in the mouse eye. These results suggest that mPins plays important roles in eye development. This work provides preliminary evidence strongly supporting a role for mPins in the asymmetric division of retinoblasts, and in the structure and functions of adult mouse retina. However, the link between the presence of mPins in different ocular compartments and the possible occurrence of asymmetric cell divisions in these compartments remains to be clarified. Further studies are required to elucidate the in vitro and in vivo functions of mPins in the developing and adult human eye.
The process of rod photoreceptor genesis, cell fate determination and differentiation is complex and multi-factorial. Previous studies have defined a model of photoreceptor differentiation that relies on intrinsic changes within the presumptive photoreceptor cells as well as changes in surrounding tissue that are extrinsic to the cell. We have used a proteomics approach to identify proteins that are dynamically expressed in the mouse retina during rod genesis and differentiation.
A series of six developmental ages from E13 to P5 were used to define changes in retinal protein expression during rod photoreceptor genesis and early differentiation. Retinal proteins were separated by isoelectric focus point and molecular weight. Gels were analyzed for changes in protein spot intensity across developmental time. Protein spots that peaked in expression at E17, P0 and P5 were picked from gels for identification. There were 239 spots that were picked for identification based on their dynamic expression during the developmental period of maximal rod photoreceptor genesis and differentiation. Of the 239 spots, 60 of them were reliably identified and represented a single protein. Ten proteins were represented by multiple spots, suggesting they were post-translationally modified. Of the 42 unique dynamically expressed proteins identified, 16 had been previously reported to be associated with the developing retina.
Our results represent the first proteomics study of the developing mouse retina that includes prenatal development. We identified 26 dynamically expressed proteins in the developing mouse retina whose expression had not been previously associated with retinal development.
A major challenge in systems biology is to develop a detailed dynamic understanding of the functions and behaviors in a particular cellular system, which depends on the elements and their inter-relationships in a specific network. Computational modeling plays an integral part in the study of network dynamics and uncovering the underlying mechanisms. Here we proposed a systematic approach that incorporates discrete dynamic modeling and experimental data to reconstruct a phenotype-specific network of cell signaling. A dynamic analysis of the insulin signaling system in liver cells provides a proof-of-concept application of the proposed methodology. Our group recently identified that double-stranded RNA-dependent protein kinase (PKR) plays an important role in the insulin signaling network. The dynamic behavior of the insulin signaling network is tuned by a variety of feedback pathways, many of which have the potential to cross talk with PKR. Given the complexity of insulin signaling, it is inefficient to experimentally test all possible interactions in the network to determine which pathways are functioning in our cell system. Our discrete dynamic model provides an in silico model framework that integrates potential interactions and assesses the contributions of the various interactions on the dynamic behavior of the signaling network. Simulations with the model generated testable hypothesis on the response of the network upon perturbation, which were experimentally evaluated to identify the pathways that function in our particular liver cell system. The modeling in combination with the experimental results enhanced our understanding of the insulin signaling dynamics and aided in generating a context-specific signaling network.
Organogenesis is regulated by a complex network of intrinsic cues, diffusible signals and cell/cell or cell/matrix interactions that drive the cells of a prospective organ to differentiate and collectively organize in three dimensions. Generating organs in vitro from embryonic stem (ES) cells may provide a simplified system to decipher how these processes are orchestrated in time and space within particular and between neighboring tissues. Recently, this field of stem cell research has also gained considerable interest for its potential applications in regenerative medicine. Among human pathologies for which stem cell-based therapy is foreseen as a promising therapeutic strategy are many retinal degenerative diseases, like retinitis pigmentosa and age-related macular degeneration. Over the last decade, progress has been made in producing ES-derived retinal cells in vitro, but engineering entire synthetic retinas was considered beyond reach. Recently however, major breakthroughs have been achieved with pioneer works describing the extraordinary self-organization of murine and human ES cells into a three dimensional structure highly resembling a retina. ES-derived retinal cells indeed assemble to form a cohesive neuroepithelial sheet that is endowed with the intrinsic capacity to recapitulate, outside an embryonic environment, the main steps of retinal morphogenesis as observed in vivo. This represents a tremendous advance that should help resolving fundamental questions related to retinogenesis. Here, we will discuss these studies, and the potential applications of such stem cell-based systems for regenerative medicine.
Retina; Optic cup; Embryonic stem cells; Retinal pigment epithelium; Three dimensional culture
The concept of “network target” has ushered in a new era in the field of traditional Chinese medicine (TCM). As a new research approach, network pharmacology is based on the analysis of network models and systems biology. Taking advantage of advancements in systems biology, a high degree of integration data analysis strategy and interpretable visualization provides deeper insights into the underlying mechanisms of TCM theories, including the principles of herb combination, biological foundations of herb or herbal formulae action, and molecular basis of TCM syndromes. In this study, we review several recent developments in TCM network pharmacology research and discuss their potential for bridging the gap between traditional and modern medicine. We briefly summarize the two main functional applications of TCM network models: understanding/uncovering and predicting/discovering. In particular, we focus on how TCM network pharmacology research is conducted and highlight different computational tools, such as network-based and machine learning algorithms, and sources that have been proposed and applied to the different steps involved in the research process. To make network pharmacology research commonplace, some basic network definitions and analysis methods are presented.
The SEGUE (Set the stage, Elicit information, Give information, Understand the patient's perspective, and End the encounter) Framework is a checklist-style rating scale to facilitate the teaching and assessment of communication skills in medical learners. It has been used for over 15 years, and it is recommended in the Accreditation Council for Graduate Medical Education toolbox of assessment methods for resident training. When it was developed, its ability to provide objective scoring was a substantial improvement over global ratings.
In this article we describe the strengths and weaknesses of the SEGUE Framework. We highlight one residency program's experience with using the SEGUE Framework to evaluate residents' communication skills. Specifically, we cite previous studies and describe our own analysis of resident interviewing performance that demonstrates how the SEGUE Framework did not distinguish between different levels of interviewing skill level in our sample.
Two case examples illustrate how the SEGUE Framework is not an ideal instrument to measure either the quality or the process of medical interviews.
Therefore, we propose a new method of contextualized assessment that builds on the SEGUE Framework. Our system evaluates discrete interviewing behaviors within the context of an ambulatory medical interview. We describe our interview structure, as well as a new instrument (the Wy-Mii, pronounced “why me”), to assess both communication and interpersonal skills. We expect that our new method of contextualized assessment will better differentiate between beginning and advanced levels of medical interviewing skills for residents.
Motivation: Combinatorial interactions of transcription factors with cis-regulatory elements control the dynamic progression through successive cellular states and thus underpin all metazoan development. The construction of network models of cis-regulatory elements, therefore, has the potential to generate fundamental insights into cellular fate and differentiation. Haematopoiesis has long served as a model system to study mammalian differentiation, yet modelling based on experimentally informed cis-regulatory interactions has so far been restricted to pairs of interacting factors. Here, we have generated a Boolean network model based on detailed cis-regulatory functional data connecting 11 haematopoietic stem/progenitor cell (HSPC) regulator genes.
Results: Despite its apparent simplicity, the model exhibits surprisingly complex behaviour that we charted using strongly connected components and shortest-path analysis in its Boolean state space. This analysis of our model predicts that HSPCs display heterogeneous expression patterns and possess many intermediate states that can act as ‘stepping stones’ for the HSPC to achieve a final differentiated state. Importantly, an external perturbation or ‘trigger’ is required to exit the stem cell state, with distinct triggers characterizing maturation into the various different lineages. By focusing on intermediate states occurring during erythrocyte differentiation, from our model we predicted a novel negative regulation of Fli1 by Gata1, which we confirmed experimentally thus validating our model. In conclusion, we demonstrate that an advanced mammalian regulatory network model based on experimentally validated cis-regulatory interactions has allowed us to make novel, experimentally testable hypotheses about transcriptional mechanisms that control differentiation of mammalian stem cells.
email@example.com or firstname.lastname@example.org or email@example.com
Supplementary data are available at Bioinformatics online.
Organogenesis of the eye is a multi-step process that starts with the formation of optic vesicles followed by invagination of the distal domain of the vesicles and the overlying lens placode resulting in morphogenesis of the optic cup. The late optic vesicle becomes patterned into distinct ocular tissues; the neural retina, retinal pigment epithelium (RPE) and optic stalk. Multiple congenital eye disorders, including anophthalmia or microphthalmia, aniridia, coloboma and retinal dysplasia, stem from disruptions in embryonic eye development. Thus, it is critical to understand the mechanisms that lead to initial specification and differentiation of ocular tissues. An accumulating number of studies demonstrate that a complex interplay between inductive signals provided by tissue-tissue interactions and cell-intrinsic factors is critical to ensure proper specification of ocular tissues as well as maintenance of RPE cell fate. While several of the extrinsic and intrinsic determinants have been identified, we are just at the beginning to understand how these signals are integrated. In addition, we know very little about the actual output of these interactions. In this chapter, we provide an update of the mechanisms controlling early steps of eye development in vertebrates, with emphasis on optic vesicle evagination, specification of neural retina and RPE at the optic vesicle stage, the process of invagination during morphogenesis of the optic cup and maintenance of the RPE cell fate.
Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods.
Several researchers have observed that the connectivity of a network alone can provide significant insights into its dynamics. Motivated by this fundamental observation, we present the signaling Petri net, a non-parametric model of cellular signaling networks, and the signaling Petri net-based simulator, a Petri net execution strategy for characterizing the dynamics of signal flow through a signaling network using token distribution and sampling. The result is a very fast method, which can analyze large-scale networks, and provide insights into the trends of molecules' activity-levels in response to an external stimulus, based solely on the network's connectivity.
We have implemented the signaling Petri net-based simulator in the PathwayOracle toolkit, which is publicly available at http://bioinfo.cs.rice.edu/pathwayoracle. Using this method, we studied a MAPK1,2 and AKT signaling network downstream from EGFR in two breast tumor cell lines. We analyzed, both experimentally and computationally, the activity level of several molecules in response to a targeted manipulation of TSC2 and mTOR-Raptor. The results from our method agreed with experimental results in greater than 90% of the cases considered, and in those where they did not agree, our approach provided valuable insights into discrepancies between known network connectivities and experimental observations.
Many cellular behaviors including growth, differentiation, and movement are influenced by external stimuli. Such external stimuli are obtained, processed, and carried to the nucleus by the signaling network—a dense network of cellular biochemical reactions. Beyond being interesting for their role in directing cellular behavior, deleterious changes in a cell's signaling network can alter a cell's responses to external stimuli, giving rise to devastating diseases such as cancer. As a result, building accurate mathematical and computational models of cellular signaling networks is a major endeavor in biology. The scale and complexity of these networks render them difficult to analyze by experimental techniques alone, which has led to the development of computational analysis methods. In this paper, we present a novel computational simulation technique that can provide qualitatively accurate predictions of the behavior of a cellular signaling network without requiring detailed knowledge of the signaling network's parameters. Our approach makes use of recent discoveries that network structure alone can determine many aspects of a network's dynamics. When compared against experimental results, our method correctly predicted 90% of the cases considered. In those where it did not agree, our approach provided valuable insights into discrepancies between known network structure and experimental observations.
Metabolic networks perform some of the most fundamental functions in living cells, including energy transduction and building block biosynthesis. While these are the best characterized networks in living systems, understanding their evolutionary history and complex wiring constitutes one of the most fascinating open questions in biology, intimately related to the enigma of life's origin itself. Is the evolution of metabolism subject to general principles, beyond the unpredictable accumulation of multiple historical accidents? Here we search for such principles by applying to an artificial chemical universe some of the methodologies developed for the study of genome scale models of cellular metabolism. In particular, we use metabolic flux constraint-based models to exhaustively search for artificial chemistry pathways that can optimally perform an array of elementary metabolic functions. Despite the simplicity of the model employed, we find that the ensuing pathways display a surprisingly rich set of properties, including the existence of autocatalytic cycles and hierarchical modules, the appearance of universally preferable metabolites and reactions, and a logarithmic trend of pathway length as a function of input/output molecule size. Some of these properties can be derived analytically, borrowing methods previously used in cryptography. In addition, by mapping biochemical networks onto a simplified carbon atom reaction backbone, we find that properties similar to those predicted for the artificial chemistry hold also for real metabolic networks. These findings suggest that optimality principles and arithmetic simplicity might lie beneath some aspects of biochemical complexity.
Metabolism is the network of biochemical reactions that transforms available resources (“inputs”) into energy currency and building blocks (“outputs”). Different organisms have different assortments of metabolic pathways and input/output requirements, reflecting their adaptation to specific environments, and to specific strategies for reproduction and survival. Here we ask whether, beneath the intricate wiring of these networks, it is possible to discern signatures of optimal (i.e., shortest and maximally efficient) pathway architectures. A systematic search for such optimal pathways between all possible pairs of input and output molecules in real organic chemistry is computationally intractable. However, we can implement such a search in a simple artificial chemistry, which roughly resembles a single atom (e.g., carbon) version of real biochemistry. We find that optimal pathways in our idealized chemistry display a logarithmic dependence of pathway length on input/output molecule size. They also display recurring topologies, including autocatalytic cycles reminiscent of ancient and highly conserved cores of real biochemistry. Finally, across all optimal pathways, we identify universally important metabolites and reactions, as well as a characteristic distribution of reaction utilization. Similar features can be observed in real metabolic networks, suggesting that arithmetic simplicity may lie beneath some aspects of biochemical complexity.