Multiscale modeling of diffusion in the early Drosophila embryo

Growth control in the Drosophila wing disk

The regulation of size and shape is a fundamental requirement of biological development and has been a subject of scientific study for centuries, but we still lack an understanding of how organisms know when to stop growing. Imaginal wing disks of the fruit fly Drosophila melanogaster, which are precursors of the adult wings, are an archetypal tissue for studying growth control. The growth of the disks is dependent on many inter- and intra-organ factors such as morphogens, mechanical forces, nutrient levels, and hormones that influence gene expression and cell growth. Extracellular signals are transduced into gene-control signals via complex signal transduction networks, and since cells typically receive many different signals, a mechanism for integrating the signals is needed. Our understanding of the effect of morphogens on tissue-level growth regulation via individual pathways has increased significantly in the last half century, but our understanding of how multiple biochemical and mechanical signals are integrated to determine whether or not a cell decides to divide is still rudimentary. Numerous fundamental questions are involved in understanding the decision-making process, and here we review the major biochemical and mechanical pathways involved in disk development with a view toward providing a basis for beginning to understand how multiple signals can be integrated at the cell level, and how this translates into growth control at the level of the imaginal disk. This article is categorized under: Analytical and Computational Methods > Computational Methods Biological Mechanisms > Cell Signaling Models of Systems Properties and Processes > Cellular Models.

Unified model of brain tissue microstructure dynamically binds diffusion and osmosis with extracellular space geometry

We present a universal model of brain tissue microstructure that dynamically links osmosis and diffusion with geometrical parameters of brain extracellular space (ECS). Our model robustly describes and predicts the nonlinear time dependency of tortuosity (λ=sqrt[D/D^{*}]) changes with very high precision in various media with uniform and nonuniform osmolarity distribution, as demonstrated by previously published experimental data (D = free diffusion coefficient, D^{*} = effective diffusion coefficient). To construct this model, we first developed a multiscale technique for computationally effective modeling of osmolarity in the brain tissue. Osmolarity differences across cell membranes lead to changes in the ECS dynamics. The evolution of the underlying dynamics is then captured by a level set method. Subsequently, using a homogenization technique, we derived a coarse-grained model with parameters that are explicitly related to the geometry of cells and their associated ECS. Our modeling results in very accurate analytical approximation of tortuosity based on time, space, osmolarity differences across cell membranes, and water permeability of cell membranes. Our model provides a unique platform for studying ECS dynamics not only in physiologic conditions such as sleep-wake cycles and aging but also in pathologic conditions such as stroke, seizure, and neoplasia, as well as in predictive pharmacokinetic modeling such as predicting medication biodistribution and efficacy and novel biomolecule development and testing.

Visualizing retinoic acid morphogen gradients

Morphogens were originally defined as secreted signaling molecules that diffuse from local sources to form concentration gradients, which specify multiple cell fates. More recently morphogen gradients have been shown to incorporate a range of mechanisms including short-range signal activation, transcriptional/translational feedback, and temporal windows of target gene induction. Many critical cell-cell signals implicated in both embryonic development and disease, such as Wnt, fibroblast growth factor (Fgf), hedgehog (Hh), transforming growth factor beta (TGFb), and retinoic acid (RA), are thought to act as morphogens, but key information on signal propagation and ligand distribution has been lacking for most. The zebrafish provides unique advantages for genetics and imaging to address gradients during early embryonic stages when morphogens help establish major body axes. This has been particularly informative for RA, where RA response elements (RAREs) driving fluorescent reporters as well as Fluorescence Resonance Energy Transfer (FRET) reporters of receptor binding have provided evidence for gradients, as well as regulatory mechanisms that attenuate noise and enhance gradient robustness in vivo. Here we summarize available tools in zebrafish and discuss their utility for studying dynamic regulation of RA morphogen gradients, through combined experimental and computational approaches.

Semi-permeable Diffusion Barriers Enhance Patterning Robustness in the C. elegans Germline

Positional information derived from local morphogen concentration plays an important role in patterning. A key question is how morphogen diffusion and gene expression regulation shape positional information into an appropriate profile with suitably low noise. We address this question using a model system--the C. elegans germline--whose regulatory network has been well characterized genetically but whose spatiotemporal dynamics are poorly understood. We show that diffusion within the germline syncytium is a critical control of stem cell differentiation and that semi-permeable diffusion barriers present at key locations make it possible--in combination with a feedback loop in the germline regulatory network--for mitotic zone size to be robust against spatial noise in Notch signaling. Spatial averaging within compartments defined by diffusion barriers is an advantageous patterning strategy, which attenuates noise while still allowing for sharp transitions between compartments. This strategy could apply to other organs.

In-silico models of stem cell and developmental systems

Understanding how developmental systems evolve over time is a key question in stem cell and developmental biology research. However, due to hurdles of existing experimental techniques, our understanding of these systems as a whole remains partial and coarse. In recent years, we have been constructing in-silico models that synthesize experimental knowledge using software engineering tools. Our approach integrates known isolated mechanisms with simplified assumptions where the knowledge is limited. This has proven to be a powerful, yet underutilized, tool to analyze the developmental process. The models provide a means to study development in-silico by altering the model’s specifications, and thereby predict unforeseen phenomena to guide future experimental trials. To date, three organs from diverse evolutionary organisms have been modeled: the mouse pancreas, the C. elegans gonad, and partial rodent brain development. Analysis and execution of the models recapitulated the development of the organs, anticipated known experimental results and gave rise to novel testable predictions. Some of these results had already been validated experimentally. In this paper, I review our efforts in realistic in-silico modeling of stem cell research and developmental biology and discuss achievements and challenges. I envision that in the future, in-silico models as presented in this paper would become a common and useful technique for research in developmental biology and related research fields, particularly regenerative medicine, tissue engineering and cancer therapeutics.

Multiscale computational models of complex biological systems

Integration of data across spatial, temporal, and functional scales is a primary focus of biomedical engineering efforts. The advent of powerful computing platforms, coupled with quantitative data from high-throughput experimental methodologies, has allowed multiscale modeling to expand as a means to more comprehensively investigate biological phenomena in experimentally relevant ways. This review aims to highlight recently published multiscale models of biological systems, using their successes to propose the best practices for future model development. We demonstrate that coupling continuous and discrete systems best captures biological information across spatial scales by selecting modeling techniques that are suited to the task. Further, we suggest how to leverage these multiscale models to gain insight into biological systems using quantitative biomedical engineering methods to analyze data in nonintuitive ways. These topics are discussed with a focus on the future of the field, current challenges encountered, and opportunities yet to be realized.

Morphogen transport

The graded distribution of morphogens underlies many of the tissue patterns that form during development. How morphogens disperse from a localized source and how gradients in the target tissue form has been under debate for decades. Recent imaging studies and biophysical measurements have provided evidence for various morphogen transport models ranging from passive mechanisms, such as free or hindered extracellular diffusion, to cell-based dispersal by transcytosis or cytonemes. Here, we analyze these transport models using the morphogens Nodal, fibroblast growth factor and Decapentaplegic as case studies. We propose that most of the available data support the idea that morphogen gradients form by diffusion that is hindered by tortuosity and binding to extracellular molecules.

Emergent complexity of the cytoskeleton: from single filaments to tissue

Despite their overwhelming complexity, living cells display a high degree of internal mechanical and functional organization which can largely be attributed to the intracellular biopolymer scaffold, the cytoskeleton. Being a very complex system far from thermodynamic equilibrium, the cytoskeleton's ability to organize is at the same time challenging and fascinating. The extensive amounts of frequently interacting cellular building blocks and their inherent multifunctionality permits highly adaptive behavior and obstructs a purely reductionist approach. Nevertheless (and despite the field's relative novelty), the physics approach has already proved to be extremely successful in revealing very fundamental concepts of cytoskeleton organization and behavior. This review aims at introducing the physics of the cytoskeleton ranging from single biopolymer filaments to multicellular organisms. Throughout this wide range of phenomena, the focus is set on the intertwined nature of the different physical scales (levels of complexity) that give rise to numerous emergent properties by means of self-organization or self-assembly.

Developmental pattern formation: insights from physics and biology

The spatial organization of cell fates during development involves the interpretation of morphogen gradients by cellular signaling cascades and transcriptional networks. Recent studies use biophysical models, genetics, and quantitative imaging to unravel how tissue-level morphogen behavior arises from subcellular events. Moreover, data from several systems show that morphogen gradients, downstream signaling, and the activity of cell-intrinsic transcriptional networks change dynamically during pattern formation. Studies from Drosophila and now also vertebrates suggest that transcriptional network dynamics are central to the generation of gene expression patterns. Together, this leads to the view that pattern formation is an emergent behavior that results from the coordination of events occurring across molecular, cellular, and tissue scales. The development of novel approaches to study this complex process remains a challenge.

The synthesis-diffusion-degradation model explains Bicoid gradient formation in unfertilized eggs

Precise formation of morphogen gradients is essential to the establishment of reproducible pattern in development. Mechanisms proposed for obtaining the requisite precision range from simple models with few parameters to more complex models involving many regulated quantities. The synthesis-diffusion-degradation (SDD) model is a relatively simple model explaining the formation of the Bicoid gradient in Drosophila melanogaster, in which the steady-state characteristic length of the gradient is determined solely by the rates of diffusion and degradation of the morphogen. In this work, we test the SDD model in unfertilized D. melanogaster eggs, which contain a single female pronucleus and lack the nuclear division cycles and other zygotic regulatory processes seen in fertilized eggs. Using two-photon live imaging as well as a novel method for quantitative imaging based on decorrelation of photoswitching waveforms, we find that the Bicoid gradient is longer and shallower in unfertilized eggs as compared to the gradient at the same time points in fertilized eggs. Using a means of measuring the Bicoid lifetime by conjugation to a photoconvertible fluorophore, we find that the lifetime is correspondingly longer in unfertilized eggs, providing qualitative and quantitative agreement with the predictions of the SDD model.

Investigating the principles of morphogen gradient formation: from tissues to cells

Morphogen gradients regulate the patterning and growth of many tissues, hence a key question is how they are established and maintained during development. Theoretical descriptions have helped to explain how gradient shape is controlled by the rates of morphogen production, spreading and degradation. These effective rates have been measured using fluorescence recovery after photobleaching (FRAP) and photoactivation. To unravel which molecular events determine the effective rates, such tissue-level assays have been combined with genetic analysis, high-resolution assays, and models that take into account interactions with receptors, extracellular components and trafficking. Nevertheless, because of the natural and experimental data variability, and the underlying assumptions of transport models, it remains challenging to conclusively distinguish between cellular mechanisms.

Mutual repression enhances the steepness and precision of gene expression boundaries

Embryonic development is driven by spatial patterns of gene expression that determine the fate of each cell in the embryo. While gene expression is often highly erratic, embryonic development is usually exceedingly precise. In particular, gene expression boundaries are robust not only against intra-embryonic fluctuations such as noise in gene expression and protein diffusion, but also against embryo-to-embryo variations in the morphogen gradients, which provide positional information to the differentiating cells. How development is robust against intra- and inter-embryonic variations is not understood. A common motif in the gene regulation networks that control embryonic development is mutual repression between pairs of genes. To assess the role of mutual repression in the robust formation of gene expression patterns, we have performed large-scale stochastic simulations of a minimal model of two mutually repressing gap genes in Drosophila, hunchback (hb) and knirps (kni). Our model includes not only mutual repression between hb and kni, but also the stochastic and cooperative activation of hb by the anterior morphogen Bicoid (Bcd) and of kni by the posterior morphogen Caudal (Cad), as well as the diffusion of Hb and Kni between neighboring nuclei. Our analysis reveals that mutual repression can markedly increase the steepness and precision of the gap gene expression boundaries. In contrast to other mechanisms such as spatial averaging and cooperative gene activation, mutual repression thus allows for gene-expression boundaries that are both steep and precise. Moreover, mutual repression dramatically enhances their robustness against embryo-to-embryo variations in the morphogen levels. Finally, our simulations reveal that diffusion of the gap proteins plays a critical role not only in reducing the width of the gap gene expression boundaries via the mechanism of spatial averaging, but also in repairing patterning errors that could arise because of the bistability induced by mutual repression.

Cellular retinoic acid-binding proteins are essential for hindbrain patterning and signal robustness in zebrafish

The vitamin A derivative retinoic acid (RA) is a morphogen that patterns the anterior-posterior axis of the vertebrate hindbrain. Cellular retinoic acid-binding proteins (Crabps) transport RA within cells to both its nuclear receptors (RARs) and degrading enzymes (Cyp26s). However, mice lacking Crabps are viable, suggesting that Crabp functions are redundant with those of other fatty acid-binding proteins. Here we show that Crabps in zebrafish are essential for posterior patterning of the hindbrain and that they provide a key feedback mechanism that makes signaling robust as they are able to compensate for changes in RA production. Of the four zebrafish Crabps, Crabp2a is uniquely RA inducible and depletion or overexpression of Crabp2a makes embryos hypersensitive to exogenous RA. Computational models confirm that Crabp2a improves robustness within a narrow concentration range that optimizes a 'robustness index', integrating spatial information along the RA morphogen gradient. Exploration of signaling parameters in our models suggests that the ability of Crabp2a to transport RA to Cyp26 enzymes for degradation is a major factor in promoting robustness. These results demonstrate a previously unrecognized requirement for Crabps in RA signaling and hindbrain development, as well as a novel mechanism for stabilizing morphogen gradients despite genetic or environmental fluctuations in morphogen availability.

Multiscale diffusion in the mitotic Drosophila melanogaster syncytial blastoderm

Despite the fundamental importance of diffusion for embryonic morphogen gradient formation in the early Drosophila melanogaster embryo, there remains controversy regarding both the extent and the rate of diffusion of well-characterized morphogens. Furthermore, the recent observation of diffusional "compartmentalization" has suggested that diffusion may in fact be nonideal and mediated by an as-yet-unidentified mechanism. Here, we characterize the effects of the geometry of the early syncytial Drosophila embryo on the effective diffusivity of cytoplasmic proteins. Our results demonstrate that the presence of transient mitotic membrane furrows results in a multiscale diffusion effect that has a significant impact on effective diffusion rates across the embryo. Using a combination of live-cell experiments and computational modeling, we characterize these effects and relate effective bulk diffusion rates to instantaneous diffusion coefficients throughout the syncytial blastoderm nuclear cycle phase of the early embryo. This multiscale effect may be related to the effect of interphase nuclei on effective diffusion, and thus we propose that an as-yet-unidentified role of syncytial membrane furrows is to temporally regulate bulk embryonic diffusion rates to balance the multiscale effect of interphase nuclei, which ultimately stabilizes the shapes of various morphogen gradients.

Differential diffusivity of Nodal and Lefty underlies a reaction-diffusion patterning system

Biological systems involving short-range activators and long-range inhibitors can generate complex patterns. Reaction-diffusion models postulate that differences in signaling range are caused by differential diffusivity of inhibitor and activator. Other models suggest that differential clearance underlies different signaling ranges. To test these models, we measured the biophysical properties of the Nodal/Lefty activator/inhibitor system during zebrafish embryogenesis. Analysis of Nodal and Lefty gradients revealed that Nodals have a shorter range than Lefty proteins. Pulse-labeling analysis indicated that Nodals and Leftys have similar clearance kinetics, whereas fluorescence recovery assays revealed that Leftys have a higher effective diffusion coefficient than Nodals. These results indicate that differential diffusivity is the major determinant of the differences in Nodal/Lefty range and provide biophysical support for reaction-diffusion models of activator/inhibitor-mediated patterning.

Multiscale models of cell signaling

Computational models of signal transduction face challenges of scale below the resolution of a single cell. Here, we organize these challenges around three key interfaces for multiscale models of cell signaling: molecules to pathways, pathways to networks, and networks to outcomes. Each interface requires its own set of computational approaches and systems-level data, and no single approach or dataset can effectively bridge all three interfaces. This suggests that realistic "whole-cell" models of signaling will need to agglomerate different model types that span critical intracellular scales. Future multiscale models will be valuable for understanding the impact of signaling mutations or population variants that lead to cellular diseases such as cancer.

Mathematics and biology: a Kantian view on the history of pattern formation theory

Driesch's statement, made around 1900, that the physics and chemistry of his day were unable to explain self-regulation during embryogenesis was correct and could be extended until the year 1972. The emergence of theories of self-organisation required progress in several areas including chemistry, physics, computing and cybernetics. Two parallel lines of development can be distinguished which both culminated in the early 1970s. Firstly, physicochemical theories of self-organisation arose from theoretical (Lotka 1910-1920) and experimental work (Bray 1920; Belousov 1951) on chemical oscillations. However, this research area gained broader acceptance only after thermodynamics was extended to systems far from equilibrium (1922-1967) and the mechanism of the prime example for a chemical oscillator, the Belousov-Zhabotinski reaction, was deciphered in the early 1970s. Secondly, biological theories of self-organisation were rooted in the intellectual environment of artificial intelligence and cybernetics. Turing wrote his The chemical basis of morphogenesis (1952) after working on the construction of one of the first electronic computers. Likewise, Gierer and Meinhardt's theory of local activation and lateral inhibition (1972) was influenced by ideas from cybernetics. The Gierer-Meinhardt theory provided an explanation for the first time of both spontaneous formation of spatial order and of self-regulation that proved to be extremely successful in elucidating a wide range of patterning processes. With the advent of developmental genetics in the 1980s, detailed molecular and functional data became available for complex developmental processes, allowing a new generation of data-driven theoretical approaches. Three examples of such approaches will be discussed. The successes and limitations of mathematical pattern formation theory throughout its history suggest a picture of the organism, which has structural similarity to views of the organic world held by the philosopher Immanuel Kant at the end of the eighteenth century.

Genesis of two-dimensional patterns in cross-gradient fields

Tissue morphogenesis is controlled by the two-dimensional patterning of gene expression in epithelial layers, that determines cell fates. The mechanisms of pattern formation involve intracellular regulatory networks controlled by paracrine and autocrine signaling. We develop a general logical scheme to deduce the morphology of two-dimensional patterns in the field of two crossed morphogen gradients enriched by the action of autocrine signaling that may subdivide expression domains in nontrivial ways. A variety of persistent patterns, either stationary or oscillatory, are generated using the various interaction schemes, some of which have been generated by a special algorithm including random inputs and selected according to suitable criteria. We give the full classification of a variety of stationary and oscillatory expression patterns in the presence of a single autocrine signal based on logical arguments. These results are further confirmed by numerical computations based on reaction-diffusion equations for morphogens and ligands and the discrete (cell-level) description of intracellular dynamics. Model simulations also elucidate transient processes, in particular interaction schemes. Different internal schemes may lead to identical persistent patterns, although relaxation may proceed in distinct ways. As an illustration of the general method, we test a particular scheme suggested by genetic studies of dynamic gene expression patterns in the follicular epithelium of the Drosophila eggshell.

Analysis of gap gene regulation in a 3D organism-scale model of the Drosophila melanogaster embryo

The axial bodyplan of Drosophila melanogaster is determined during a process called morphogenesis. Shortly after fertilization, maternal bicoid mRNA is translated into Bicoid (Bcd). This protein establishes a spatially graded morphogen distribution along the anterior-posterior (AP) axis of the embryo. Bcd initiates AP axis determination by triggering expression of gap genes that subsequently regulate each other's expression to form a precisely controlled spatial distribution of gene products. Reaction-diffusion models of gap gene expression on a 1D domain have previously been used to infer complex genetic regulatory network (GRN) interactions by optimizing model parameters with respect to 1D gap gene expression data. Here we construct a finite element reaction-diffusion model with a realistic 3D geometry fit to full 3D gap gene expression data. Though gap gene products exhibit dorsal-ventral asymmetries, we discover that previously inferred gap GRNs yield qualitatively correct AP distributions on the 3D domain only when DV-symmetric initial conditions are employed. Model patterning loses qualitative agreement with experimental data when we incorporate a realistic DV-asymmetric distribution of Bcd. Further, we find that geometry alone is insufficient to account for DV-asymmetries in the final gap gene distribution. Additional GRN optimization confirms that the 3D model remains sensitive to GRN parameter perturbations. Finally, we find that incorporation of 3D data in simulation and optimization does not constrain the search space or improve optimization results.

Modeling cellular processes in 3D

Recent advances in photonic imaging and fluorescent protein technology offer unprecedented views of molecular space-time dynamics in living cells. At the same time, advances in computing hardware and software enable modeling of ever more complex systems, from global climate to cell division. As modeling and experiment become more closely integrated we must address the issue of modeling cellular processes in 3D. Here, we highlight recent advances related to 3D modeling in cell biology. While some processes require full 3D analysis, we suggest that others are more naturally described in 2D or 1D. Keeping the dimensionality as low as possible reduces computational time and makes models more intuitively comprehensible; however, the ability to test full 3D models will build greater confidence in models generally and remains an important emerging area of cell biological modeling.

Distinguishing graded and ultrasensitive signalling cascade kinetics by the shape of morphogen gradients in Drosophila

Recently, signalling gradients in cascades of two-state reaction-diffusion systems were described as a model for understanding key biochemical mechanisms that underlie development and differentiation processes in the Drosophila embryo. Diffusion-trapping at the exterior of the cell membrane triggers the mitogen-activated protein kinase (MAPK) cascade to relay an appropriate signal from the membrane to the inner part of the cytosol, whereupon another diffusion-trapping mechanism involving the nucleus reads out this signal to trigger appropriate changes in gene expression. Proposed mathematical models exhibit equilibrium distributions consistent with experimental measurements of key spatial gradients in these processes. A significant property of the formulation is that the signal is assumed to be relayed from one system to the next in a linear fashion. However, the MAPK cascade often exhibits nonlinear dose-response properties and the final remark of Berezhkovskii et al. (2009) is that this assumption remains an important property to be tested experimentally, perhaps via a new quantitative assay across multiple genetic backgrounds. In anticipation of the need to be able to sensibly interpret data from such experiments, here we provide a complementary analysis that recovers existing formulae as a special case but is also capable of handling nonlinear functional forms. Predictions of linear and nonlinear signal relays and, in particular, graded and ultrasensitive MAPK kinetics, are compared.

The Drosophila gap gene network is composed of two parallel toggle switches

Drosophila “gap” genes provide the first response to maternal gradients in the early fly embryo. Gap genes are expressed in a series of broad bands across the embryo during first hours of development. The gene network controlling the gap gene expression patterns includes inputs from maternal gradients and mutual repression between the gap genes themselves. In this study we propose a modular design for the gap gene network, involving two relatively independent network domains. The core of each network domain includes a toggle switch corresponding to a pair of mutually repressive gap genes, operated in space by maternal inputs. The toggle switches present in the gap network are evocative of the phage lambda switch, but they are operated positionally (in space) by the maternal gradients, so the synthesis rates for the competing components change along the embryo anterior-posterior axis. Dynamic model, constructed based on the proposed principle, with elements of fractional site occupancy, required 5–7 parameters to fit quantitative spatial expression data for gap gradients. The identified model solutions (parameter combinations) reproduced major dynamic features of the gap gradient system and explained gap expression in a variety of segmentation mutants.

Formation of morphogen gradients: local accumulation time

Spatial regulation of cell differentiation in embryos can be provided by morphogen gradients, which are defined as the concentration fields of molecules that control gene expression. For example, a cell can use its surface receptors to measure the local concentration of an extracellular ligand and convert this information into a corresponding change in its transcriptional state. We characterize the time needed to establish a steady-state gradient in problems with diffusion and degradation of locally produced chemical signals. A relaxation function is introduced to describe how the morphogen concentration profile approaches its steady state. This function is used to obtain a local accumulation time that provides a time scale that characterizes relaxation to steady state at an arbitrary position within the patterned field. To illustrate the approach we derive local accumulation times for a number of commonly used models of morphogen gradient formation.

The formation of the Bicoid morphogen gradient requires protein movement from anteriorly localized mRNA

New quantitative data show that the Bicoid morphogen gradient is generated from a dynamic localized source and that protein gradient formation requires protein movement along the anterior-posterior axis.

The Bicoid morphogen gradient directs the patterning of cell fates along the anterior-posterior axis of the syncytial Drosophila embryo and serves as a paradigm of morphogen-mediated patterning. The simplest models of gradient formation rely on constant protein synthesis and diffusion from anteriorly localized source mRNA, coupled with uniform protein degradation. However, currently such models cannot account for all known gradient characteristics. Recent work has proposed that bicoid mRNA spatial distribution is sufficient to produce the observed protein gradient, minimizing the role of protein transport. Here, we adapt a novel method of fluorescent in situ hybridization to quantify the global spatio-temporal dynamics of bicoid mRNA particles. We determine that >90% of all bicoid mRNA is continuously present within the anterior 20% of the embryo. bicoid mRNA distribution along the body axis remains nearly unchanged despite dynamic mRNA translocation from the embryo core to the cortex. To evaluate the impact of mRNA distribution on protein gradient dynamics, we provide detailed quantitative measurements of nuclear Bicoid levels during the formation of the protein gradient. We find that gradient establishment begins 45 minutes after fertilization and that the gradient requires about 50 minutes to reach peak levels. In numerical simulations of gradient formation, we find that incorporating the actual bicoid mRNA distribution yields a closer prediction of the observed protein dynamics compared to modeling protein production from a point source at the anterior pole. We conclude that the spatial distribution of bicoid mRNA contributes to, but cannot account for, protein gradient formation, and therefore that protein movement, either active or passive, is required for gradient formation.

The Bicoid protein gradient plays a crucial role in determining the anterior body pattern of Drosophila embryos. This gradient is the classic example of morphogen-mediated patterning of a developing metazoan and serves as a major topic for mathematical modeling. Accurate modeling of the gradient requires a detailed account of the underlying bicoid mRNA distribution. The classic model holds that mRNA protein gradient arises via protein diffusion from mRNA localized at the anterior of the developing egg. In contrast, recent proposals suggest that an mRNA gradient generates the protein gradient without protein movement. In this study, we introduce a novel mRNA quantification method for Drosophila embryos, which allows us to visualize each individual mRNA particle accurately in whole embryos. We demonstrate that all but a few mRNA particles are confined to the anterior 20% of the egg, and consequently that the protein must move in order to establish a gradient. We further report that the mRNA distribution is highly dynamic during the time of protein synthesis. In numerical simulations, we show that incorporating realistic spatial locations of the individual source mRNA molecules throughout the developmental period is necessary to accurately model the experimentally observed protein gradient dynamics.

Assessment of Transport Mechanisms Underlying the Bicoid Morphogen Gradient

Morphogen gradients dictate the spatial patterning of multicellular organisms and are established via transport mechanisms. One of the best-characterized morphogens, Bicoid, acts as a polarity determinant in the Drosophila embryo through spatial-temporal control of gap gene expression. The prevailing model for establishment of the gradient has been localized anterior translation, subsequent diffusion, and spatially uniform degradation, consistent with the observed exponential anterior-posterior decay. However, a recent direct measurement of the Bicoid diffusion coefficient via fluorescence recovery after photobleaching (FRAP) resulted in a surprisingly low estimate, which challenged the prevailing model and led to more complicated active transport models. Here, we reassessed this conclusion using a detailed computational model of the FRAP experiment and analysis. In our model, we found disagreement between the input diffusion coefficient and the resulting estimated diffusion coefficient, as measured by previous methods. By using the model to reproduce the original data, we estimate that Bicoid's mitotic diffusion coefficient is 3-fold larger than the originally reported value. Thus, the long-standing diffusive transport model still holds.

Modelling the Bicoid gradient

Morphogen gradients provide embryonic tissues with positional information by inducing target genes at different concentration thresholds and thus at different positions. The Bicoid morphogen gradient in Drosophila melanogaster embryos has recently been analysed quantitatively, yet how it forms remains a matter of controversy. Several biophysical models that rely on production, diffusion and degradation have been formulated to account for the observed dynamics of the Bicoid gradient, but no one model can account for all its characteristics. Here, we discuss how existing data on this gradient fit the various proposed models and what aspects of gradient formation these models fail to explain. We suggest that knowing a few additional parameters, such as the lifetime of Bicoid, would help to identify and develop better models of Bicoid gradient formation.

A compartmental model for the bicoid gradient

The anterior region of the Drosophila embryo is patterned by the concentration gradient of the homeodomain transcription factor bicoid (Bcd). The Bcd gradient was the first identified morphogen gradient and continues to be a subject of intense research at multiple levels, from the mechanisms of RNA localization in the oocyte to the evolution of the Bcd-mediated patterning events in multiple Drosophila species. Critical assessment of the mechanisms of the Bcd gradient formation requires biophysical models of the syncytial embryo. Most of the proposed models rely on reaction-diffusion equations, but their formulation and applicability at high nuclear densities is a nontrivial task. We propose a straightforward alternative in which the syncytial blastoderm is approximated by a periodic arrangement of well-mixed compartments: a single nucleus and an associated cytoplasmic region. We formulate a compartmental model, constrain its parameters by experimental data, and demonstrate that it provides an adequate description of the Bcd gradient dynamics.