Reconstruction of human mammary tissues in a mouse model

Establishing a model system that more accurately recapitulates both normal and neoplastic breast epithelial development in rodents is central to studying human breast carcinogenesis. However, the inability of human breast epithelial cells to colonize mouse mammary fat pads is problematic. Considering that the human breast is a more fibrous tissue than is the adipose-rich stroma of the murine mammary gland, our group sought to bypass the effects of the rodent microenvironment through incorporation of human stromal fibroblasts. We have been successful in reproducibly recreating functionally normal breast tissues from reduction mammoplasty tissues, in what we term the human-in-mouse (HIM) model. Here we describe our relatively simple and inexpensive techniques for generating this orthotopic xenograft model. Whether the model is to be applied for understanding normal human breast development or tumorigenesis, investigators with minimal animal surgery skills, basic cell culture techniques and access to human breast tissue will be able to generate humanized mouse glands within 3 months. Clearing the mouse of its endogenous epithelium with subsequent stromal humanization takes 1 month. The subsequent implantation of co-mixed human epithelial cells and stromal cells occurs 2 weeks after humanization, so investigators should expect to observe the desired outgrowths 2 months afterward. As a whole, this model system has the potential to improve the understanding of crosstalk between tissue stroma and the epithelium as well as factors involved in breast stem cell biology tumor initiation and progression.     

Development of a finger biomechanical model and its considerations

The development of a biomechanical model for a human finger is faced with many challenges, such as extensor mechanism complexity, statistical indeterminacy and suitability of computational processes. Motivation for this work was to develop a computer model that is able to predict the internal loading patterns of tendons and joint surfaces experienced by the human finger, while mitigating these challenges. Proposed methodology was based on a non-linear optimising mathematical technique with a criterion of boundary conditions and equality equations, maximised against unknown parameters to reduce statistical indeterminacy. Initial validation was performed via the simulation of one dynamic and two static postures case studies. Past models and experiments were used, based on published literature, to verify the proposed model's methodology and results. The feasibility of the proposed methodology was deemed satisfactory as the simulated results were concordant with in-vivo results for the extrinsic flexors.     

The research of constructing dynamic cognition model based on brain network

Estimating the functional interactions and connections between brain regions to corresponding process in cognitive, behavioral and psychiatric domains is a central pursuit for understanding the human connectome. Few studies have examined the effects of dynamic evolution on cognitive processing and brain activation using brain network model in scalp electroencephalography (EEG) data. Aim of this study was to investigate the brain functional connectivity and construct dynamic programing model from EEG data and to evaluate a possible correlation between topological characteristics of the brain connectivity and cognitive evolution processing. Here, functional connectivity between brain regions is defined as the statistical dependence between EEG signals in different brain areas and is typically determined by calculating the relationship between regional time series using wavelet coherence. We present an accelerated dynamic programing algorithm to construct dynamic cognitive model that we found that spatially distributed regions coherence connection difference, the topologic characteristics with which they can transfer information, producing temporary network states. Our findings suggest that brain dynamics give rise to variations in complex network properties over time after variation audio stimulation, dynamic programing model gives the dynamic evolution processing at different time and frequency. In this paper, by applying a new construct approach to understand whole brain network dynamics, firstly, brain network is constructed by wavelet coherence, secondly, different time active brain regions are selected by network topological characteristics and minimum spanning tree. Finally, dynamic evolution model is constructed to understand cognitive process by dynamic programing algorithm, this model is applied to the auditory experiment, results showed that, quantitatively, more correlation was observed after variation audio stimulation, the EEG function connection dynamic evolution model on cognitive processing is feasible with wavelet coherence EEG recording.     

Human liver model systems in a dish

The human adult liver has a multi‐cellular structure consisting of large lobes subdivided into lobules containing portal triads and hepatic cords lined by specialized blood vessels. Vital hepatic functions include filtering blood, metabolizing drugs, and production of bile and blood plasma proteins like albumin, among many other functions, which are generally dependent on the location or zone in which the hepatocyte resides in the liver. Due to the liver's intricate structure, there are many challenges to design differentiation protocols to generate more mature functional hepatocytes from human stem cells and maintain the long‐term viability and functionality of primary hepatocytes. To this end, recent advancements in three‐dimensional (3D) stem cell culture have accelerated the generation of a human miniature liver system, also known as liver organoids, with polarized epithelial cells, supportive cell types and extra‐cellular matrix deposition by translating knowledge gained in studies of animal organogenesis and regeneration. To facilitate the efforts to study human development and disease using in vitro hepatic models, a thorough understanding of state‐of‐art protocols and underlying rationales is essential. Here, we review rapidly evolving 3D liver models, mainly focusing on organoid models differentiated from human cells.     

Generation of a functional humanized Delta-like ligand 4 transgenic mouse model

Humanized mouse models are important tools in many areas of biological drug development including, within oncology research, the development of antagonistic antibodies that have the potential to block tumor growth by controlling vascularization and are key to the generation of in vivo proof-of-concept efficacy data. However, due to cross reactivity between human antibodies and mouse target such studies regularly require mouse models expressing only the human version of the target molecule. Such humanized knock-in/knock-out, KIKO, models are dependent upon the generation of homozygous mice expressing only the human molecule, compensating for loss of the mouse form. However, KIKO strategies can fail to generate homozygous mice, even though the human form is expressed and the endogenous mouse locus is correctly targeted. A typical strategy for generating KIKO mice is by ATG fusion where the human cDNA is inserted downstream of the endogenous mouse promoter elements. However, when adopting this strategy it is possible that the mouse promoter fails to express the human form in a manner compensating for loss of the mouse form or alternatively the human protein is incompatible in the context of the mouse pathway being investigated. So to understand more around the biology of KIKO models, and to overcome our failure with a number of ATG fusion strategies, we developed a range of humanized models focused on Delta-like 4 (Dll4), a target where we initially failed to generate a humanized model. By adopting a broader biologic strategy, we successfully generated a humanized DLL4 KIKO which led to a greater understanding of critical biological aspects for consideration when developing humanized models.     

A neurally plausible model of the dynamics of motion integration in smooth eye pursuit based on recursive Bayesian estimation

In this study, we describe a model of motion integration in smooth eye pursuit based on a recursive Bayesian estimation process, which displays a dynamic behaviour qualitatively similar to the dynamics of the motion integration process observed experimentally, both psychophysically in humans and monkeys, and physiologically in monkeys. By formulating the model as an approximate version of a Kalman filter algorithm, we have been able to show that it can be put into a neurally plausible, distributed recurrent form which coarsely corresponds to the recurrent circuitry of visual cortical areas V1 and MT. The model thus provides further support for the notion that the motion integration process is based on a form of Bayesian estimation, as has been suggested by many psychophysical studies, and moreover suggests that the observed dynamic properties of this process are the result of the recursive nature of the motion estimation.     

Atherosclerotic Plaque Destabilization in Mice: A Comparative Study

Atherosclerosis-associated diseases are the main cause of mortality and morbidity in western societies. The progression of atherosclerosis is a dynamic process evolving from early to advanced lesions that may become rupture-prone vulnerable plaques. Acute coronary syndromes are the clinical manifestation of life-threatening thrombotic events associated with high-risk vulnerable plaques. Hyperlipidemic mouse models have been extensively used in studying the mechanisms controlling initiation and progression of atherosclerosis. However, the understanding of mechanisms leading to atherosclerotic plaque destabilization has been hampered by the lack of proper animal models mimicking this process. Although various mouse models generate atherosclerotic plaques with histological features of human advanced lesions, a consensus model to study atherosclerotic plaque destabilization is still lacking. Hence, we studied the degree and features of plaque vulnerability in different mouse models of atherosclerotic plaque destabilization and find that the model based on the placement of a shear stress modifier in combination with hypercholesterolemia represent with high incidence the most human like lesions compared to the other models.     

A novel approach to modelling and simulating the contact behaviour between a human hand model and a deformable object

A deeper understanding of biomechanical behaviour of human hands becomes fundamental for any human hand-operated activities. The integration of biomechanical knowledge of human hands into product design process starts to play an increasingly important role in developing an ergonomic product-to-user interface for products and systems requiring high level of comfortable and responsive interactions. Generation of such precise and dynamic models can provide scientific evaluation tools to support product and system development through simulation. This type of support is urgently required in many applications such as hand skill training for surgical operations, ergonomic study of a product or system developed and so forth. The aim of this work is to study the contact behaviour between the operators' hand and a hand-held tool or other similar contacts, by developing a novel and precise nonlinear 3D finite element model of the hand and by investigating the contact behaviour through simulation. The contact behaviour is externalised by solving the problem using the bi-potential method. The human body's biomechanical characteristics, such as hand deformity and structural behaviour, have been fully modelled by implementing anisotropic hyperelastic laws. A case study is given to illustrate the effectiveness of the approach.     

Ecotone formation induced by the effects of tidal flooding: A conceptual model of the mud flat-coastal wetland ecosystem

The boundary between mud flat and coastal wetland ecosystems is highly productive and a haven of considerable biodiversity. It is also embedded in a highly dynamic environment and can be easily destabilised by environmental changes, invasive species, and human activity. Thus, understanding the processes which govern the formation of this ecotone is important both for conservation and economic reasons. In this study we introduce a simple conceptual model for this joint ecosystem, which demonstrates that the interaction between tidal flooding and habitat elevation is able to produce an ecotone with similar characteristics to that observed in empirical studies. In particular, the transition from mud flat to vegetated state is locally abrupt, occurring at a critical threshold elevation, but, on broader spatial scales can occur over a range of elevations determined by the variability in high tide water levels. Additionally, the model shows the potential for regime shifts, resulting from periods of unusual weather or the invasion of a fast growing, or flood resistant, species.     

Prediction of peptide reactivity with human IVIg through a knowledge-based approach

The prediction of antibody-protein (antigen) interactions is very difficult due to the huge variability that characterizes the structure of the antibodies. The region of the antigen bound to the antibodies is called epitope. Experimental data indicate that many antibodies react with a panel of distinct epitopes (positive reaction). The Challenge 1 of DREAM5 aims at understanding whether there exists rules for predicting the reactivity of a peptide/epitope, i.e., its capability to bind to human antibodies. DREAM 5 provided a training set of peptides with experimentally identified high and low reactivities to human antibodies. On the basis of this training set, the participants to the challenge were asked to develop a predictive model of reactivity. A test set was then provided to evaluate the performance of the model implemented so far.We developed a logistic regression model to predict the peptide reactivity, by facing the challenge as a machine learning problem. The initial features have been generated on the basis of the available knowledge and the information reported in the dataset. Our predictive model had the second best performance of the challenge. We also developed a method, based on a clustering approach, able to "in-silico" generate a list of positive and negative new peptide sequences, as requested by the DREAM5 "bonus round" additional challenge.The paper describes the developed model and its results in terms of reactivity prediction, and highlights some open issues concerning the propensity of a peptide to react with human antibodies.     

Characterizing the amplitude dynamics of the human core-temperature circadian rhythm using a stochastic-dynamic model

Two measures, amplitude and phase, have been used to describe the characteristics of the endogenous human circadian pacemaker, a biological clock located in the hypothalamus. Although many studies of change in circadian phase with respect to different stimuli have been conducted, the physiologic implications of the amplitude changes (dynamics) of the pacemaker are unknown. It is known that phase changes of the human circadian pacemaker have a significant impact on sleep timing and content, hormone secretion, subjective alertness and neurobehavioral performance. However, the changes in circadian amplitude with respect to different stimuli are less well documented. Although amplitude dynamics of the human circadian pacemaker are observed in physiological rhythms such as plasma cortisol, plasma melatonin and core temperature data, currently methods are not available to accurately characterize the amplitude dynamics from these rhythms. Of the three rhythms core temperature is the only reliable variable that can be monitored continuously in real time with a high sampling rate. To characterize the amplitude dynamics of the circadian pacemaker we propose a stochastic-dynamic model of core temperature data that contains both stochastic and dynamic characteristics. In this model the circadian component that has a dynamic characteristic is represented as a perturbation solution of the van der Pol equation and the thermoregulatory response in the data that has a stochastic characteristic is represented as a first-order autoregressive process. The model parameters are estimated using data with a maximum likelihood procedure and the goodness-of-fit measures along with the associated standard error of the estimated parameters provided inference about the amplitude dynamics of the pacemaker. Using this model we analysed core temperature data from an experiment designed to exhibit amplitude dynamics. We found that the circadian pacemaker recovers slowly to an equilibrium level following amplitude suppression. In humans this reaction to perturbation from equilibrium value has potential physiological implications.     

Spike correlations in a songbird agree with a simple markov population model

The relationships between neural activity at the single-cell and the population levels are of central importance for understanding neural codes. In many sensory systems, collective behaviors in large cell groups can be described by pairwise spike correlations. Here, we test whether in a highly specialized premotor system of songbirds, pairwise spike correlations themselves can be seen as a simple corollary of an underlying random process. We test hypotheses on connectivity and network dynamics in the motor pathway of zebra finches using a high-level population model that is independent of detailed single-neuron properties. We assume that neural population activity evolves along a finite set of states during singing, and that during sleep population activity randomly switches back and forth between song states and a single resting state. Individual spike trains are generated by associating with each of the population states a particular firing mode, such as bursting or tonic firing. With an overall modification of one or two simple control parameters, the Markov model is able to reproduce observed firing statistics and spike correlations in different neuron types and behavioral states. Our results suggest that song- and sleep-related firing patterns are identical on short time scales and result from random sampling of a unique underlying theme. The efficiency of our population model may apply also to other neural systems in which population hypotheses can be tested on recordings from small neuron groups.     

Tracking and Predicting Human Somatic Cell Reprogramming Using Nuclear Characteristics

Reprogramming of human somatic cells to induced pluripotent stem cells (iPSCs) generates valuable resources for disease modeling, toxicology, cell therapy, and regenerative medicine. However, the reprogramming process can be stochastic and inefficient, creating many partially reprogrammed intermediates and non-reprogrammed cells in addition to fully reprogrammed iPSCs. Much of the work to identify, evaluate, and enrich for iPSCs during reprogramming relies on methods that fix, destroy, or singularize cell cultures, thereby disrupting each cell’s microenvironment. Here, we develop a micropatterned substrate that allows for dynamic live-cell microscopy of hundreds of cell subpopulations undergoing reprogramming while preserving many of the biophysical and biochemical cues within the cells’ microenvironment. On this substrate, we were able to both watch and physically confine cells into discrete islands during the reprogramming of human somatic cells from skin biopsies and blood draws obtained from healthy donors. Using high-content analysis, we identified a combination of eight nuclear characteristics that can be used to generate a computational model to predict the progression of reprogramming and distinguish partially reprogrammed cells from those that are fully reprogrammed. This approach to track reprogramming in situ using micropatterned substrates could aid in biomanufacturing of therapeutically relevant iPSCs and be used to elucidate multiscale cellular changes (cell-cell interactions as well as subcellular changes) that accompany human cell fate transitions.     

Understanding the melanocyte distribution in human epidermis: an agent-based computational model approach

The strikingly even color of human skin is maintained by the uniform distribution of melanocytes among keratinocytes in the basal layer of the human epidermis. In this work, we investigated three possible hypotheses on the mechanism by which the melanocytes and keratinocytes organize themselves to generate this pattern. We let the melanocyte migration be aided by (1) negative chemotaxis due to a substance produced by the melanocytes themselves, or (2) positive chemotaxis due to a substance produced by keratinocytes lacking direct physical contact with a melanocyte, or (3) positive chemotaxis due to a substance produced by keratinocytes in a distance-to-melanocytes dependent manner. The three hypotheses were implemented in an agent-based computational model of cellular interactions in the basal layer of the human epidermis. We found that they generate mutually exclusive predictions that can be tested by existing experimental protocols. This model forms a basis for further understanding of the communication between melanocytes and other skin cells in skin homeostasis.     

Drosophila: a "model" model system to study neurodegeneration

The fruit fly, Drosophila melanogaster, is a powerful model genetic organism that has been used since the turn of the previous century in the study of complex biological problems. In the last decade, numerous researchers have focused their attention on understanding neurodegenerative diseases by utilizing this model system. Numerous Drosophila mutants have been isolated that profoundly affect neural viability and integrity of the nervous system with age. Additionally, many transgenic strains have been developed as models of human disease conditions. We review the existing Drosophila neurodegenerative mutants and transgenic disease models, and discuss the role of the fruit fly in therapeutic development for neurodegenerative diseases.     

Switching from a mechanistic model to a continuous model to study at different scales the effect of vine growth on the dynamic of a powdery mildew epidemic

Background and Aims

Epidemiological simulation models coupling plant growth with the dispersal and disease dynamics of an airborne plant pathogen were devised for a better understanding of host–pathogen dynamic interactions and of the capacity of grapevine development to modify the progress of powdery mildew epidemics.

Methods

The first model is a complex discrete mechanistic model (M-model) that explicitly incorporates the dynamics of host growth and the development and dispersion of the pathogen at the vine stock scale. The second model is a simpler ordinary differential equations (ODEs) compartmental SEIRT model (C-model) handling host growth (foliar surface) and the ontogenic resistance of the leaves. With the M-model various levels of vine development are simulated under three contrasting climatic scenarios and the relationship between host and disease variables are examined at key periods in the epidemic process. The ability of the C-model to retrieve the main dynamics of the disease for a range of vine growth given by the M-model is investigated.

Key Results

The M-model strengthens experimental results observed regarding the effect of the rate of leaf emergence and of the number of leaves at flowering on the severity of the disease. However, it also underlines strong variations of the dynamics of disease depending on the vigour and indirectly on the climatic scenarios. The C-model could be calibrated by using the M-model provided that different parameters before and after shoot topping and for various vigour levels and inoculation time are used. Biologically relevant estimations of the parameters that could be used for its extension to the vineyard scale are obtained.

Conclusions

The M-model is able to generate a wide range of growth scenarios with a strong impact on disease evolution. The C-model is a promising tool to be used at a larger scale.     

Epigenetic metaphors: an interdisciplinary translation of encoding and decoding

Looking at the new and often disputed science of epigenetics, we examined the challenges faced by scientists when they communicate scientific research to the public. We focused on the use of metaphors to illustrate notions of epigenetics and genetics. We studied the “encoding” by epigeneticists and “decoding” in focus groups with diverse backgrounds. We observed considerable overlap in the dominant metaphors favored by both researchers and the lay public. However, the groups differed markedly in their interpretations of which metaphors aided understanding or not. We conclude by discussing the role of metaphors and their interpretations in the context of a shift from pre-deterministic genomic metaphors to more active, dynamic and nuanced epigenetic metaphors. These reflections on the choice of metaphors and differences in encoding/decoding are important for science communication and scientific boundary-maintenance.     

An integrative model of evolutionary covariance: a symposium on body shape in fishes

A major direction of current and future biological research is to understand how multiple, interacting functional systems coordinate in producing a body that works. This understanding is complicated by the fact that organisms need to work well in multiple environments, with both predictable and unpredictable environmental perturbations. Furthermore, organismal design reflects a history of past environments and not a plan for future environments. How complex, interacting functional systems evolve, then, is a truly grand challenge. In accepting the challenge, an integrative model of evolutionary covariance is developed. The model combines quantitative genetics, functional morphology/physiology, and functional ecology. The model is used to convene scientists ranging from geneticists, to physiologists, to ecologists, to engineers to facilitate the emergence of body shape in fishes as a model system for understanding how complex, interacting functional systems develop and evolve. Body shape of fish is a complex morphology that (1) results from many developmental paths and (2) functions in many different behaviors. Understanding the coordination and evolution of the many paths from genes to body shape, body shape to function, and function to a working fish body in a dynamic environment is now possible given new technologies from genetics to engineering and new theoretical models that integrate the different levels of biological organization (from genes to ecology).