Systems biotechnology of animal cells: the road to prediction

A central concern of biopharmaceutical R&D is the production of sufficient quantities of recombinant products from manufacturing processes based on animal cell culture. The way in which bioprocess researchers have addressed this question experienced a tremendous shift over the years, progressing from almost empirical to more rational approaches. A step further is the application of systems biotechnology: recent technological advances for large-scale cell state characterization and creative methods for host cell modeling are becoming crucial for next-generation bioprocess optimization. Here we provide an overview of the main trends towards this goal, with a focus on metabolic models as central scaffolds for data integration and prediction of bioprocess outcomes.     

Four-dimensional imaging with virtual reality to quantitatively explore jigsaw puzzle-like morphogenesis of Arabidopsis cotyledon pavement cells

In most dicotyledonous plants, leaf pavement cells exhibit complex jigsaw puzzle-like cell morphogenesis during leaf expansion. Although detailed molecular biological information and mathematical modeling of this jigsaw puzzle-like cell morphogenesis are now available, a full understanding of this process remains elusive. Recent reports have highlighted the importance of three-dimensional (3D) structures (i.e., anticlinal and periclinal cell wall) in understanding the mechanical models that describe this morphogenetic process. We believe that it is important to acquire 3D shapes of pavement cells over time, i.e., acquire and analyze four-dimensional (4D) information when studying the relationship between mechanical modeling and simulations and the actual cell shape. In this report, we have developed a framework to capture and analyze 4D morphological information of Arabidopsis thaliana cotyledon pavement cells by using both direct water immersion observations and computational image analyses, including segmentation, surface modeling, virtual reality and morphometry. The 4D cell models allowed us to perform time-lapse 3D morphometrical analysis, providing detailed quantitative information about changes in cell growth rate and shape, with cellular complexity observed to increase during cell growth. The framework should enable analysis of various phenotypes (e.g., mutants) in greater detail, especially in the 3D deformation of the cotyledon surface, and evaluation of theoretical models that describe pavement cell morphogenesis using computational simulations. Additionally, our accurate and high-throughput acquisition of growing cell structures should be suitable for use in generating in silico model cell structures.     

Quantitative modeling perspectives on the ErbB system of cell regulatory processes

The complexities of the processes involved in ErbB-mediated regulation of cellular phenotype are broadly appreciated, so much so that it might be reasonably argued that this highly studied system provided significant impetus for the systems perspective on cell signaling processes in general. Recent years have seen major advances in the level of characterization of the ErbB system as well as our ability to make measurements of the system. This new data provides significant new insight, while at the same time creating new challenges for making quantitative statements and predictions with certainty. Here, we discuss recent advances in each of these directions and the interplay between them, with a particular focus on quantitative modeling approaches to interpret data and provide predictive power. Our discussion follows the sequential order of ErbB pathway activation, beginning with considerations of receptor/ligand interactions and dynamics, proceeding to the generation of intracellular signals, and ending with determination of cellular phenotype. As discussed herein, these processes become increasingly difficult to describe or interpret in terms of traditional models, and we review emerging methodologies to address this complexity.     

A phase-contrast microscopy-based method for modeling the mechanical behavior of mesenchymal stem cells

We present three-dimensional (3D) finite element (FE) models of single, mesenchymal stem cells (MSCs), generated from images obtained by optical phase-contrast microscopy and used to quantify the structural responses of the studied cells to externally applied mechanical loads. Mechanical loading has been shown to affect cell morphology and structure, phenotype, motility and other biological functions. Cells experience mechanical loads naturally, yet under prolonged or sizable loading, damage and cell death may occur, which motivates research regarding the structural behavior of loaded cells. For example, near the weight-bearing boney prominences of the buttocks of immobile persons, tissues may become highly loaded, eventually leading to massive cell death that manifests as pressure ulcers. Cell-specific computational models have previously been developed by our group, allowing simulations of cell deformations under compressive or stretching loads. These models were obtained by reconstructing specific cell structures from series of 2D fluorescence, confocal image-slices, requiring cell-specific fluorescent-staining protocols and costly (confocal) microscopy equipment. Alternative modeling approaches represent cells simply as half-spheres or half-ellipsoids (i.e. idealized geometries), which neglects the curvature details of the cell surfaces associated with changes in concentrations of strains and stresses. Thus, we introduce here for the first time an optical image-based FE modeling, where loads are simulated on reconstructed 3D geometrical cell models from a single 2D, phase-contrast image. Our novel modeling method eliminates the need for confocal imaging and fluorescent staining preparations (both expensive), and makes cell-specific FE modeling affordable and accessible to the biomechanics community. We demonstrate the utility of this cost-effective modeling method by performing simulations of compression of MSCs embedded in a gel.     

Organoid technologies meet genome engineering

Three‐dimensional (3D) stem cell differentiation cultures recently emerged as a novel model system for investigating human embryonic development and disease progression in vitro, complementing existing animal and two‐dimensional (2D) cell culture models. Organoids, the 3D self‐organizing structures derived from pluripotent or somatic stem cells, can recapitulate many aspects of structural organization and functionality of their in vivo organ counterparts, thus holding great promise for biomedical research and translational applications. Importantly, faithful recapitulation of disease and development processes relies on the ability to modify the genomic contents in organoid cells. The revolutionary genome engineering technologies, CRISPR/Cas9 in particular, enable investigators to generate various reporter cell lines for prompt validation of specific cell lineages as well as to introduce disease‐associated mutations for disease modeling. In this review, we provide historical overviews, and discuss technical considerations, and potential future applications of genome engineering in 3D organoid models.     

Organ-on-chip models: Implications in drug discovery and clinical applications

Before a lead compound goes through a clinical trial, preclinical studies utilize two-dimensional (2D) in vitro models and animal models to study the pharmacodynamics and pharmacokinetics of that lead compound. However, these current preclinical studies may not accurately represent the efficacy and safety of a lead compound in humans, as there has been a high failure rate of drugs that enter clinical trials. All of these failures and the associated costs demonstrate a need for more representative models of human organ systems for screening in the preclinical phase of drug development. In this study, we review the recent advances in in vitro modeling including three-dimensional (3D) organoids, 3D microfabrication, and 3D bioprinting for various organs including the heart, kidney, lung, gastrointestinal tract (intestine–gut–stomach), liver, placenta, adipose, retina, bone, and brain as well as multiorgan models. The availability of organ-on-chip models provides a wealth of opportunities to understand the pathogenesis of human diseases and provide a potentially better model to screen a drug, as these models utilize a dynamic 3D environment similar to the human body. Although there are limitations of organ-on-chip models, the emergence of new technologies have refined their capability for translational research as well as precision medicine.     

From spatial-data to 3D models of the developing human brain

Visualisation and interpretation of gene expression data have been crucial to advances in our understanding of mechanisms underlying early brain development. As most developmental processes involve complex changes in size, shape and structure, spatial-data can most readily provide information at multiple levels (cell type, cell location in relation to tissue organisation or body axes, etc.), that can be related to these complex changes. Although three-dimensional (3D) spatial-data are ideal, the restricted availability of suitable tissues makes it difficult to generate these for genes expressed at early human fetal stages. Mapping gene expression data to representative 3D models facilitates combinatorial analysis of multiple expression patterns but does not overcome the problems of sparsely sampled data in time and space. Here we describe software that allows 3D domains to be reconstructed by interpolating between sparse 2D gene expression patterns that have been mapped to 3D representative models of corresponding human developmental stages. A set of procedures are proposed to infer expression domains in these gaps. The procedures, which are connected in a serial way, include components clustering, components tracking, shape matching and points interpolation. Each procedure consists of a graphical user interface and a set of algorithms. Results on exemplar gene data are provided.     

Novel advances in the design of three-dimensional bio-scaffolds to control cell fate: translation from 2D to 3D

Recreating the most critical aspects of the native extracellular matrix (ECM) is fundamental to understand and control the processes regulating cell fate and cell function. From the ill-defined complexity to the controlled simplicity, we discuss the different strategies that are being carried out by scientists worldwide to achieve the latest advances in the sophistication of three-dimensional (3D) scaffolds, stressing their impact on cell biology, tissue engineering and regenerative medicine. Synthetic and naturally derived polymers like polyethylene glycol, alginate, agarose, etc., together with micro- and nanofabrication techniques are allowing the creation of 3D models where biophysical and biochemical variables can be modified with high precision, orthogonality and even in real-time.     

Impact of Dimensionality and Network Disruption on Microrheology of Cancer Cells in 3D Environments

Dimensionality is a fundamental component that can have profound implications on the characteristics of physical systems. In cell biology, however, the majority of studies on cell physical properties, from rheology to force generation to migration, have been performed on 2D substrates, and it is not clear how a more realistic 3D environment influences cell properties. Here, we develop an integrated approach and demonstrate the combination of mitochondria-tracking microrheology, microfluidics, and Brownian dynamics simulations to explore the impact of dimensionality on intracellular mechanics and on the effects of intracellular disruption. Additionally, we consider both passive thermal and active motor-driven processes within the cell and demonstrate through modeling how active internal fluctuations are modulated via dimensionality. Our results demonstrate that metastatic breast cancer cells (MDA-MB-231) exhibit more solid-like internal motions in 3D compared to 2D, and actin network disruption via Cytochalasin D has a more pronounced effect on internal cell fluctuations in 2D. Our computational results and modeling show that motor-induced active stress fluctuations are enhanced in 2D, leading to increased local intracellular particle fluctuations and apparent fluid-like behavior.     

Circulating tumor cell-derived organoids: Current challenges and promises in medical research and precision medicine

Traditional 2D cell cultures do not accurately recapitulate tumor heterogeneity, and insufficient human cell lines are available. Patient-derived xenograft (PDX) models more closely mimic clinical tumor heterogeneity, but are not useful for high-throughput drug screening. Recently, patient-derived organoid cultures have emerged as a novel technique to fill this critical need. Organoids maintain tumor tissue heterogeneity and drug-resistance responses, and thus are useful for high-throughput drug screening. Among various biological tissues used to produce organoid cultures, circulating tumor cells (CTCs) are promising, due to relative ease of ascertainment. CTC-derived organoids could help to acquire relevant genetic and epigenetic information about tumors in real time, and screen and test promising drugs. This could reduce the need for tissue biopsies, which are painful and may be difficult depending on the tumor location. In this review, we have focused on advances in CTC isolation and organoid culture methods, and their potential applications in disease modeling and precision medicine.     

3D Single Molecule Tracking with Multifocal Plane Microscopy Reveals Rapid Intercellular Transferrin Transport at Epithelial Cell Barriers

The study of intracellular transport pathways at epithelial cell barriers that line diverse tissue sites is fundamental to understanding tissue homeostasis. A major impediment to investigating such processes at the subcellular level has been the lack of imaging approaches that support fast three-dimensional (3D) tracking of cellular dynamics in thick cellular specimens. Here, we report significant advances in multifocal plane microscopy and demonstrate 3D single molecule tracking of rapid protein dynamics in a 10 micron thick live epithelial cell monolayer. We have investigated the transferrin receptor (TfR) pathway, which is not only essential for iron delivery but is also of importance for targeted drug delivery across cellular barriers at specific body sites, such as the brain that is impermeable to blood-borne substances. Using multifocal plane microscopy, we have discovered a cellular process of intercellular transfer involving rapid exchange of Tf molecules between two adjacent cells in the monolayer. Furthermore, 3D tracking of Tf molecules at the lateral plasma membrane has led to the identification of different modes of endocytosis and exocytosis, which exhibit distinct temporal and intracellular spatial trajectories. These results reveal the complexity of the 3D trafficking pathways in epithelial cell barriers. The methods and approaches reported here can enable the study of fast 3D cellular dynamics in other cell systems and models, and underscore the importance of developing advanced imaging technologies to study such processes.     

Modeling the tumor extracellular matrix: Tissue engineering tools repurposed towards new frontiers in cancer biology

Cancer progression is mediated by complex epigenetic, protein and structural influences. Critical among them are the biochemical, mechanical and architectural properties of the extracellular matrix (ECM). In recognition of the ECM's important role, cancer biologists have repurposed matrix mimetic culture systems first widely used by tissue engineers as new tools for in vitro study of tumor models. In this review we discuss the pathological changes in tumor ECM, the limitations of 2D culture on both traditional and polyacrylamide hydrogel surfaces in modeling these characteristics and advances in both naturally derived and synthetic scaffolds to facilitate more complex and controllable 3D cancer cell culture. Studies using naturally derived matrix materials like Matrigel and collagen have produced significant findings related to tumor morphogenesis and matrix invasion in a 3D environment and the mechanotransductive signaling that mediates key tumor–matrix interaction. However, lack of precise experimental control over important matrix factors in these matrices have increasingly led investigators to synthetic and semi-synthetic scaffolds that offer the engineering of specific ECM cues and the potential for more advanced experimental manipulations. Synthetic scaffolds composed of poly(ethylene glycol) (PEG), for example, facilitate highly biocompatible 3D culture, modular bioactive features like cell-mediated matrix degradation and complete independent control over matrix bioactivity and mechanics. Future work in PEG or similar reductionist synthetic matrix systems should enable the study of increasingly complex and dynamic tumor–ECM relationships in the hopes that accurate modeling of these relationships may reveal new cancer therapeutics targeting tumor progression and metastasis.     

Uncovering the statistical physics of 3D chromosomal organization using data-driven modeling

In recent years, much effort has been devoted to understanding the three-dimensional (3D) organization of the genome and how genomic structure mediates nuclear function. The development of experimental techniques that combine DNA proximity ligation with high-throughput sequencing, such as Hi-C, have substantially improved our knowledge about chromatin organization. Numerous experimental advancements, not only utilizing DNA proximity ligation but also high-resolution genome imaging (DNA tracing), have required theoretical modeling to determine the structural ensembles consistent with such data. These 3D polymer models of the genome provide an understanding of the physical mechanisms governing genome architecture. Here, we present an overview of the recent advances in modeling the ensemble of 3D chromosomal structures by employing the maximum entropy approach combined with polymer physics. Particularly, we discuss the minimal chromatin model (MiChroM) along with the “maximum entropy genomic annotations from biomarkers associated with structural ensembles” (MEGABASE) model, which have been remarkably successful in the accurate modeling of chromosomes consistent with both Hi-C and DNA-tracing data.     

MODELING THREE-DIMENSIONAL MORPHOLOGICAL STRUCTURES USING SPHERICAL HARMONICS

Quantifying morphological shape is a fundamental issue in evolutionary biology. Recent technological advances (e.g., confocal microscopy, laser scanning, computer tomography) have made the capture of detailed three-dimensional (3D) morphological structure easy and cost-effective. In this article, we develop a 3D analytic framework (SPHARM—spherical harmonics) for modeling the shapes of complex morphological structures from continuous surface maps that can be produced by these technologies. Because the traditional SPHARM methodology has limitations in several of its processing steps, we present new algorithms for two SPHARM processing steps: spherical parameterization and SPHARM registration. These new algorithms allow for the numerical characterization of a much larger class of 3D models. We demonstrate the effectiveness of the method by applying it to modeling the cerci of Enallagma damselflies.     

Liver diseases in the dish: iPSC and organoids as a new approach to modeling liver diseases

Liver diseases negatively impact the quality of life and survival of patients, and often require liver transplantation in cases that progress to organ failure. Understanding the cellular and molecular mechanisms of liver development and pathogenesis has been a challenging task, in part for the lack of adequate cellular models directly relevant to the human diseases.Recent technological advances in the stem cell field have shown the potentiality of induced pluripotent stem cells (iPSC) and liver organoids as the next generation tool to model in vitro liver diseases. Hepatocyte-like cells and cholangiocyte are currently being generated from skin fibroblasts and mononuclear blood cells reprogrammed into iPSC and have been successfully used for disease modeling, drug testing and gene editing, with the hope to be able to find application also in regenerative medicine. Protocols to generate other liver cell types are still under development, but the field is advancing rapidly. On the other end, liver cells can now be isolated from liver specimens (liver explants or liver biopsies) and cultured in specific conditions to form polarized 3D organoids. The purpose of this review is to summarize all these recent technological advances and their potential applications but also to analyze the current issues to be addressed before the technology can reach its full potential.     

A new generation of predictive models: The added value of hybrid models for manufacturing processes of therapeutic proteins

Due to the lack of complete understanding of metabolic networks and reaction pathways, establishing a universal mechanistic model for mammalian cell culture processes remains a challenge. Contrarily, data-driven approaches for modeling these processes lack extrapolation capabilities. Hybrid modeling is a technique that exploits the synergy between the two modeling methods. Although mammalian cell cultures are among the most relevant processes in biotechnology and indeed looks ideal for hybrid modeling, their application has only been proposed but never developed in the literature. This study provides a quantitative assessment of the improvement brought by hybrid models with respect to the state-of-the-art statistical predictive models in the context of therapeutic protein production. This is illustrated using a dataset obtained from a 3.5 L fed-batch experiment. With the goal to robustly define the process design space, hybrid models reveal a superior capability to predict the time evolution of different process variables using only the initial and process conditions in comparison to the statistical models. Hybrid models not only feature more accurate prediction results but also demonstrate better robustness and extrapolation capabilities. For the future application, this study highlights the added value of hybrid modeling for model-based process optimization and design of experiments.     

Computational fluid dynamics modeling,a novel,and effective approach for developing scalable cell therapy manufacturing processes

Induced pluripotent stem cells (iPSCs) hold great potential to generate novel, curative cell therapy products. However, current methods to generate these novel therapies lack scalability, are labor-intensive, require a large footprint, and are not suited to meet clinical and commercial demands. Therefore, it is necessary to develop scalable manufacturing processes to accommodate the generation of high-quality iPSC derivatives under controlled conditions. The current scale-up methods used in cell therapy processes are based on empirical, geometry-dependent methods that do not accurately represent the hydrodynamics of 3D bioreactors. These methods require multiple iterations of scale-up studies, resulting in increased development cost and time. Here we show a novel approach using computational fluid dynamics modeling to effectively scale-up cell therapy manufacturing processes in 3D bioreactors. Using a GMP-compatible iPSC line, we translated and scaled-up a small-scale cardiomyocyte differentiation process to a 3-L computer-controlled bioreactor in an efficient manner, showing comparability in both systems.     

Cellular signaling identifiability analysis: A case study

Two primary purposes for mathematical modeling in cell biology are (1) simulation for making predictions of experimental outcomes and (2) parameter estimation for drawing inferences from experimental data about unobserved aspects of biological systems. While the former purpose has become common in the biological sciences, the latter is less common, particularly when studying cellular and subcellular phenomena such as signaling—the focus of the current study. Data are difficult to obtain at this level. Therefore, even models of only modest complexity can contain parameters for which the available data are insufficient for estimation. In the present study, we use a set of published cellular signaling models to address issues related to global parameter identifiability. That is, we address the following question: assuming known time courses for some model variables, which parameters is it theoretically impossible to estimate, even with continuous, noise-free data? Following an introduction to this problem and its relevance, we perform a full identifiability analysis on a set of cellular signaling models using DAISY (Differential Algebra for the Identifiability of SYstems). We use our analysis to bring to light important issues related to parameter identifiability in ordinary differential equation (ODE) models. We contend that this is, as of yet, an under-appreciated issue in biological modeling and, more particularly, cell biology.     

Homology modeling: an important tool for the drug discovery

In the last decades, homology modeling has become a popular tool to access theoretical three-dimensional (3D) structures of molecular targets. So far several 3D models of proteins have been built by this technique and used in a great diversity of structural biology studies. But are those models consistent enough with experimental structures to make this technique an effective and reliable tool for drug discovery? Here we present, briefly, the fundamentals and current state-of-the-art of the homology modeling techniques used to build 3D structures of molecular targets, which experimental structures are not available in databases, and list some of the more important works, using this technique, available in literature today. In many cases those studies have afforded successful models for the drug design of more selective agonists/antagonists to the molecular targets in focus and guided promising experimental works, proving that, when the appropriate templates are available, useful models can be built using some of the several software available today for this purpose. Limitations of the experimental techniques used to solve 3D structures allied to constant improvements in the homology modeling software will maintain the need for theoretical models, establishing the homology modeling as a fundamental tool for the drug discovery.