Systematic approaches to dissect biological processes in stem cells by image-based screening

High-throughput RNAi or small molecule screens have proven to be powerful methodologies for the systematic dissection of cellular processes. In model organisms and cell lines, large-scale screens have identified key components of many cellular pathways and helped to identify novel targets in disease-relevant pathways. Image-based high-content screening has become an increasingly important tool in high-throughput screening, enabling changes in phenotype characteristics, such as cell morphology and cell differentiation, to be monitored. In this review, we discuss the use of image-based screening approaches to explore the behavior of adult, embryonic, and induced pluripotent stem cells. First, we review how current pluripotency and differentiation assays can be adapted to high-throughput formats. We then describe general aspects of image-based screening of cells and present an outlook on challenges for screening stem cells.     

Outcome modeling techniques for prostate cancer radiotherapy: Data,models, and validation

Prostate cancer is a frequently diagnosed malignancy worldwide and radiation therapy is a first-line approach in treating localized as well as locally advanced cases. The limiting factor in modern radiotherapy regimens is dose to normal structures, an excess of which can lead to aberrant radiation-induced toxicities. Conversely, dose reduction to spare adjacent normal structures risks underdosing target volumes and compromising local control. As a result, efforts aimed at predicting the effects of radiotherapy could invaluably optimize patient treatments by mitigating such toxicities and simultaneously maximizing biochemical control. In this work, we review the types of data, frameworks and techniques used for prostate radiotherapy outcome modeling. Consideration is given to clinical and dose-volume metrics, such as those amassed by the QUANTEC initiative, and also to newer methods for the integration of biological and genetic factors to improve prediction performance. We furthermore highlight trends in machine learning that may help to elucidate the complex pathophysiological mechanisms of tumor control and radiation-induced normal tissue side effects.     

Cancer immunotherapy, mathematical modeling and optimal control

Clinical immunologists, among other problems, routinely face a question: what is the best time and dose for a certain therapeutic agent to be administered to the patient in order to decrease/eradicate the pathological condition? In cancer immunotherapies the therapeutic agent is something able to elicit an immune response against cancer. The immune response has its own dynamics that depends on the immunogenicity of the therapeutic agent and on the duration of the immune response. The question then is "how can we decide when and how much of the drug to inject so to have a prolonged and effective immune response to the cancer?". This question can be addressed in mathematical terms in two stages: first one construct a mathematical model describing the cancer-immune interaction and secondly one applies the theory of optimal control to determine when and to which extent to stimulate the immune system by means of an immunotherapeutic agent administered in discrete variable doses within the therapeutic period. The solution of this mathematical problem is described and discussed in this article. We show that the method employed can be applied to find the optimal protocol in a variety of clinical problems where the kinetics of the drug or treatment and its influence on the physiologic/pathologic functions have been described by a system of ordinary differential equations.     

An augmented iterative method for identifying a stress-free reference configuration in image-based biomechanical modeling

Continued advances in computational power and methods have enabled image-based biomechanical modeling to become an important tool in basic science, diagnostic and therapeutic medicine, and medical device design. One of the many challenges of this approach, however, is identification of a stress-free reference configuration based on in vivo images of loaded and often prestrained or residually stressed soft tissues and organs. Fortunately, iterative methods have been proposed to solve this inverse problem, among them Sellier’s method. This method is particularly appealing because it is easy to implement, convergences reasonably fast, and can be coupled to nearly any finite element package. By means of several practical examples, however, we demonstrate that in its original formulation Sellier’s method is not optimally fast and may not converge for problems with large deformations. Fortunately, we can also show that a simple, inexpensive augmentation of Sellier’s method based on Aitken’s delta-squared process can not only ensure convergence but also significantly accelerate the method.     

Blood-brain barrier-on-a-chip for brain disease modeling and drug testing

The blood-brain barrier (BBB) is an interface between cerebral blood and the brain parenchyma. As a gate keeper, BBB regulates passage of nutrients and exogeneous compounds. Owing to this highly selective barrier, many drugs targeting brain diseases are not likely to pass through the BBB. Thus, a large amount of time and cost have been paid for the development of BBB targeted therapeutics. However, many drugs validated in in vitro models and animal models have failed in clinical trials primarily due to the lack of an appropriate BBB model. Human BBB has a unique cellular architecture. Different physiologies between human and animal BBB hinder the prediction of drug responses. Therefore, a more physiologically relevant alternative BBB model needs to be developed. In this review, we summarize major features of human BBB and current BBB models and describe organ-on-chip models for BBB modeling and their applications in neurological complications.     

Applications of patient-specific induced pluripotent stem cells; focused on disease modeling, drug screening and therapeutic potentials for liver disease

The recent advances in the induced pluripotent stem cell (iPSC) research have significantly changed our perspectives on regenerative medicine by providing researchers with a unique tool to derive disease-specific stem cells for study. In this review, we describe the human iPSC generation from developmentally diverse origins (i.e. endoderm-, mesoderm-, and ectoderm- tissue derived human iPSCs) and multistage hepatic differentiation protocols, and discuss both basic and clinical applications of these cells including disease modeling, drug toxicity screening/drug discovery, gene therapy and cell replacement therapy.     

Automated image-based cytometry with fluorescence-stained specimens

The combination of digitized microscopy, algorithms for object recognition and fluorescent labeling is a promising approach for reliable, quick, automated and cost-effective screening of clinical specimens. We describe two conceptually different algorithms for detecting objects in fluorescence microscopic images. One, which is partially automated, compares a mask that represents a typical object with every position in the image; the other, which is fully automated, calculates threshold intensities to segment the image into regions of objects and background. Applications of the algorithms in conjunction with a prototype image-based cytometer are demonstrated for determining the DNA ploidy distribution of cultured human endometrial cells and determining the DNA ploidy distribution and the fraction of cells expressing the E6 antigen of human papilloma virus serotypes 16 and 18 in a PAP smear. The encouraging results from this study suggest that automated image-based cytometry utilizing fluorescent stains will be a valuable asset for clinical screening.     

Unsupervised modeling of cell morphology dynamics for time-lapse microscopy

Analysis of cellular phenotypes in large imaging data sets conventionally involves supervised statistical methods, which require user-annotated training data. This paper introduces an unsupervised learning method, based on temporally constrained combinatorial clustering, for automatic prediction of cell morphology classes in time-resolved images. We applied the unsupervised method to diverse fluorescent markers and screening data and validated accurate classification of human cell phenotypes, demonstrating fully objective data labeling in image-based systems biology.     

Designing specific protein-protein interactions using computation, experimental library screening, or integrated methods

Given the importance of protein-protein interactions for nearly all biological processes, the design of protein affinity reagents for use in research, diagnosis or therapy is an important endeavor. Engineered proteins would ideally have high specificities for their intended targets, but achieving interaction specificity by design can be challenging. There are two major approaches to protein design or redesign. Most commonly, proteins and peptides are engineered using experimental library screening and/or in vitro evolution. An alternative approach involves using protein structure and computational modeling to rationally choose sequences predicted to have desirable properties. Computational design has successfully produced novel proteins with enhanced stability, desired interactions and enzymatic function. Here we review the strengths and limitations of experimental library screening and computational structure-based design, giving examples where these methods have been applied to designing protein interaction specificity. We highlight recent studies that demonstrate strategies for combining computational modeling with library screening. The computational methods provide focused libraries predicted to be enriched in sequences with the properties of interest. Such integrated approaches represent a promising way to increase the efficiency of protein design and to engineer complex functionality such as interaction specificity.     

Multi-scale modeling for sustainable chemical production

With recent advances in metabolic engineering, it is now technically possible to produce a wide portfolio of existing petrochemical products from biomass feedstock. In recent years, a number of modeling approaches have been developed to support the engineering and decision-making processes associated with the development and implementation of a sustainable biochemical industry. The temporal and spatial scales of modeling approaches for sustainable chemical production vary greatly, ranging from metabolic models that aid the design of fermentative microbial strains to material and monetary flow models that explore the ecological impacts of all economic activities. Research efforts that attempt to connect the models at different scales have been limited. Here, we review a number of existing modeling approaches and their applications at the scales of metabolism, bioreactor, overall process, chemical industry, economy, and ecosystem. In addition, we propose a multi-scale approach for integrating the existing models into a cohesive framework. The major benefit of this proposed framework is that the design and decision-making at each scale can be informed, guided, and constrained by simulations and predictions at every other scale. In addition, the development of this multi-scale framework would promote cohesive collaborations across multiple traditionally disconnected modeling disciplines to achieve sustainable chemical production.     

Microbial metabolism as an evolutionary response: the cybernetic approach to modeling

The growth and metabolic capabilities of microorganisms depend on their interactions with the culture medium. Many media contain two or more key substrates, and an organism may have different preferences for the components. Microorganisms adjust their preferences according to the prevailing conditions so as to favor their own survival. Cybernetic modeling describes this evolutionary strategy by defining a goal that an organism tries to attain optimally at all times. The goal is often, but not always, maximization of growth, and it may require the cells to manipulate their metabolic processes in response to changing environmental conditions. The cybernetic approach overcomes some of the limitations of metabolic control analysis (MCA), but it does not substitute MCA. Here we review the development of the cybernetic modeling of microbial metabolism, how it may be combined with MCA, and what improvements are needed to make it a viable technique for industrial fermentation processes.     

PREDICT modeling and in-silico screening for G-protein coupled receptors

G-protein coupled receptors (GPCRs) are a major group of drug targets for which only one x-ray structure is known (the nondrugable rhodopsin), limiting the application of structure-based drug discovery to GPCRs. In this paper we present the details of PREDICT, a new algorithmic approach for modeling the 3D structure of GPCRs without relying on homology to rhodopsin. PREDICT, which focuses on the transmembrane domain of GPCRs, starts from the primary sequence of the receptor, simultaneously optimizing multiple 'decoy' conformations of the protein in order to find its most stable structure, culminating in a virtual receptor-ligand complex. In this paper we present a comprehensive analysis of three PREDICT models for the dopamine D2, neurokinin NK1, and neuropeptide Y Y1 receptors. A shorter discussion of the CCR3 receptor model is also included. All models were found to be in good agreement with a large body of experimental data. The quality of the PREDICT models, at least for drug discovery purposes, was evaluated by their successful utilization in in-silico screening. Virtual screening using all three PREDICT models yielded enrichment factors 9-fold to 44-fold better than random screening. Namely, the PREDICT models can be used to identify active small-molecule ligands embedded in large compound libraries with an efficiency comparable to that obtained using crystal structures for non-GPCR targets.     

Human iPSC-derived cardiomyocytes and tissue engineering strategies for disease modeling and drug screening

Improved methodologies for modeling cardiac disease phenotypes and accurately screening the efficacy and toxicity of potential therapeutic compounds are actively being sought to advance drug development and improve disease modeling capabilities. To that end, much recent effort has been devoted to the development of novel engineered biomimetic cardiac tissue platforms that accurately recapitulate the structure and function of the human myocardium. Within the field of cardiac engineering, induced pluripotent stem cells (iPSCs) are an exciting tool that offer the potential to advance the current state of the art, as they are derived from somatic cells, enabling the development of personalized medical strategies and patient specific disease models. Here we review different aspects of iPSC-based cardiac engineering technologies. We highlight methods for producing iPSC-derived cardiomyocytes (iPSC-CMs) and discuss their application to compound efficacy/toxicity screening and in vitro modeling of prevalent cardiac diseases. Special attention is paid to the application of micro- and nano-engineering techniques for the development of novel iPSC-CM based platforms and their potential to advance current preclinical screening modalities.     

Common features of the conformations of antigen-binding loops in immunoglobulins and application to modeling loop conformations.

Using database screening techniques we have examined the relationship between antigen-binding loops in immunoglobulins, and regions of similar conformation in other protein families. The conformations of most antigen-binding loops are not unique to immunoglobulins. But in many cases, the geometrical relationship between the loop and the peptides flanking it differs between the immunoglobulins and other structures with the same loop. We assess model building by data base screening, compared with that based on canonical structures.     

Mathematical analysis and modeling of motion direction selectivity in the retina

Motion detection is one of the most important and primitive computations performed by our visual system. Specifically in the retina, ganglion cells producing motion direction-selective responses have been addressed by different disciplines, such as mathematics, neurophysiology and computational modeling, since the beginnings of vision science. Although a number of studies have analyzed theoretical and mathematical considerations for such responses, a clear picture of the underlying cellular mechanisms is only recently emerging. In general, motion direction selectivity is based on a non-linear asymmetric computation inside a receptive field differentiating cell responses between preferred and null direction stimuli. To what extent can biological findings match these considerations? In this review, we outline theoretical and mathematical studies of motion direction selectivity, aiming to map the properties of the models onto the neural circuitry and synaptic connectivity found in the retina. Additionally, we review several compartmental models that have tried to fill this gap. Finally, we discuss the remaining challenges that computational models will have to tackle in order to fully understand the retinal motion direction-selective circuitry.     

Understanding HPV tests and their appropriate applications

Greater understanding of the role played by human papillomavirus (HPV) in the causation of disease has led to the development of an increasing number of HPV tests with different characteristics. The bewildering choice facing healthcare professionals and providers is daunting. Clearly, HPV testing is no longer simply of research interest, but can provide information that can be used for individual patient management and at the population level for cervical screening and vaccine surveillance. This review aims to provide the background to the development of HPV tests, to explain the different technologies and to discuss the challenges of the application of these optimally in the varied contexts of disease management. Few HPV tests are approved for clinical use and it is important that clinicians understand which test can be utilized, in what circumstances, with what specimens and the meaning of the report issued. HPV testing is no longer applicable only to cervical disease, and we have suggested additional areas, such as the oropharynx, in which HPV testing services might be implemented in the near future. New tests will continue to emerge and we have identified some of the indirect measures of HPV activity, or biomarkers, that could help in the risk stratification of HPV infection and associated disease. The challenges relating to the optimal application of the various HPV technologies are compounded by the lack of evidence regarding their performance in vaccinated populations. Currently published work, including modelling studies, has been undertaken in non‐immunized populations. We therefore end by addressing the issues regarding appropriate strategies and tests for immunized populations.     

Climate change,woodpeckers, and forests: Current trends and future modeling needs

The structure and composition of forest ecosystems are expected to shift with climate‐induced changes in precipitation, temperature, fire, carbon mitigation strategies, and biological disturbance. These factors are likely to have biodiversity implications. However, climate‐driven forest ecosystem models used to predict changes to forest structure and composition are not coupled to models used to predict changes to biodiversity. We proposed integrating woodpecker response (biodiversity indicator) with forest ecosystem models. Woodpeckers are a good indicator species of forest ecosystem dynamics, because they are ecologically constrained by landscape‐scale forest components, such as composition, structure, disturbance regimes, and management activities. In addition, they are correlated with forest avifauna community diversity. In this study, we explore integrating woodpecker and forest ecosystem climate models. We review climate–woodpecker models and compare the predicted responses to observed climate‐induced changes. We identify inconsistencies between observed and predicted responses, explore the modeling causes, and identify the models pertinent to integration that address the inconsistencies. We found that predictions in the short term are not in agreement with observed trends for 7 of 15 evaluated species. Because niche constraints associated with woodpeckers are a result of complex interactions between climate, vegetation, and disturbance, we hypothesize that the lack of adequate representation of these processes in the current broad‐scale climate–woodpecker models results in model–data mismatch. As a first step toward improvement, we suggest a conceptual model of climate–woodpecker–forest modeling for integration. The integration model provides climate‐driven forest ecosystem modeling with a measure of biodiversity while retaining the feedback between climate and vegetation in woodpecker climate change modeling.