MDCK cystogenesis driven by cell stabilization within computational analogues

Assessment of Tilapia Skin Collagen for Biomedical Research Applications in Comparison with Mammalian Collagen

Collagen is an important material for biomedical research, but using mammalian tissue-derived collagen carries the risk of zoonotic disease transmission. Marine organisms, such as farmed tilapia, have emerged as a safe alternative source of collagen for biomedical research. However, the tilapia collagen products for biomedical research are rare, and their biological functions remain largely unexamined. In this study, we characterized a commercial tilapia skin collagen using SDS-PAGE and fibril formation assays and evaluated its effects on skin fibroblast adhesion, proliferation, and migration, comparing it with commercial collagen from rat tails, porcine skin, and bovine skin. The results showed that tilapia skin collagen is a type I collagen, similar to rat tail collagen, and has a faster fibril formation rate and better-promoting effects on cell migration than porcine and bovine skin collagen. We also confirmed its application in a 3D culture for kidney cells' spherical cyst formation, fibroblast-induced gel contraction, and tumor spheroid interfacial invasion. Furthermore, we demonstrated that the freeze-dried tilapia skin collagen scaffold improved wound closure in a mouse excisional wound model, similar to commercial porcine or bovine collagen wound dressings. In conclusion, tilapia skin collagen is an ideal biomaterial for biomedical research.

CartoCell, a high-content pipeline for 3D image analysis, unveils cell morphology patterns in epithelia

Decades of research have not yet fully explained the mechanisms of epithelial self-organization and 3D packing. Single-cell analysis of large 3D epithelial libraries is crucial for understanding the assembly and function of whole tissues. Combining 3D epithelial imaging with advanced deep-learning segmentation methods is essential for enabling this high-content analysis. We introduce CartoCell, a deep-learning-based pipeline that uses small datasets to generate accurate labels for hundreds of whole 3D epithelial cysts. Our method detects the realistic morphology of epithelial cells and their contacts in the 3D structure of the tissue. CartoCell enables the quantification of geometric and packing features at the cellular level. Our single-cell cartography approach then maps the distribution of these features on 2D plots and 3D surface maps, revealing cell morphology patterns in epithelial cysts. Additionally, we show that CartoCell can be adapted to other types of epithelial tissues.

Epithelial tissue confinement inhibits cell growth and leads to volume-reducing divisions

Cell proliferation is a central process in tissue development, homeostasis, and disease, yet how proliferation is regulated in the tissue context remains poorly understood. Here, we introduce a quantitative framework to elucidate how tissue growth dynamics regulate cell proliferation. Using MDCK epithelial monolayers, we show that a limiting rate of tissue expansion creates confinement that suppresses cell growth; however, this confinement does not directly affect the cell cycle. This leads to uncoupling between rates of cell growth and division in epithelia and, thereby, reduces cell volume. Division becomes arrested at a minimal cell volume, which is consistent across diverse epithelia in vivo. Here, the nucleus approaches the minimum volume capable of packaging the genome. Loss of cyclin D1-dependent cell-volume regulation results in an abnormally high nuclear-to-cytoplasmic volume ratio and DNA damage. Overall, we demonstrate how epithelial proliferation is regulated by the interplay between tissue confinement and cell-volume regulation.

A hybrid physics-based and data-driven framework for cellular biological systems: Application to the morphogenesis of organoids

How cells orchestrate their cellular functions remains a crucial question to unravel how they organize in different patterns. We present a framework based on artificial intelligence to advance the understanding of how cell functions are coordinated spatially and temporally in biological systems. It consists of a hybrid physics-based model that integrates both mechanical interactions and cell functions with a data-driven model that regulates the cellular decision-making process through a deep learning algorithm trained on image data metrics. To illustrate our approach, we used data from 3D cultures of murine pancreatic ductal adenocarcinoma cells (PDAC) grown in Matrigel as tumor organoids. Our approach allowed us to find the underlying principles through which cells activate different cell processes to self-organize in different patterns according to the specific microenvironmental conditions. The framework proposed here expands the tools for simulating biological systems at the cellular level, providing a novel perspective to unravel morphogenetic patterns.

Computational approaches for simulating luminogenesis

Lumens, liquid-filled cavities surrounded by polarized tissue cells, are elementary units involved in the morphogenesis of organs. Theoretical modeling and computations, which can integrate various factors involved in biophysics of morphogenesis of cell assembly and lumens, may play significant roles to elucidate the mechanisms in formation of such complex tissue with lumens. However, up to present, it has not been documented well what computational approaches or frameworks can be applied for this purpose and how we can choose the appropriate approach for each problem. In this review, we report some typical lumen morphologies and basic mechanisms for the development of lumens, focusing on three keywords - mechanics, hydraulics and geometry - while outlining pros and cons of the current main computational strategies. We also describe brief guidance of readouts, i.e., what we should measure in experiments to make the comparison with the model's assumptions and predictions.

A 3D multi-agent-based model for lumen morphogenesis: the role of the biophysical properties of the extracellular matrix

The correct function of many organs depends on proper lumen morphogenesis, which requires the orchestration of both biological and mechanical aspects. However, how these factors coordinate is not yet fully understood. Here, we focus on the development of a mechanistic model for computationally simulating lumen morphogenesis. In particular, we consider the hydrostatic pressure generated by the cells' fluid secretion as the driving force and the density of the extracellular matrix as regulators of the process. For this purpose, we develop a 3D agent-based-model for lumen morphogenesis that includes cells' fluid secretion and the density of the extracellular matrix. Moreover, this computer-based model considers the variation in the biological behavior of cells in response to the mechanical forces that they sense. Then, we study the formation of the lumen under different-mechanical scenarios and conclude that an increase in the matrix density reduces the lumen volume and hinders lumen morphogenesis. Finally, we show that the model successfully predicts normal lumen morphogenesis when the matrix density is physiological and aberrant multilumen formation when the matrix density is excessive.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00366-022-01654-1.

Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues

A main goal of mathematical and computational oncology is to develop quantitative tools to determine the most effective therapies for each individual patient. This involves predicting the right drug to be administered at the right time and at the right dose. Such an approach is known as precision medicine. Mathematical modelling can play an invaluable role in the development of such therapeutic strategies, since it allows for relatively fast, efficient and inexpensive simulations of a large number of treatment schedules in order to find the most effective. This review is a survey of mathematical models that explicitly take into account the spatial architecture of three-dimensional tumours and address tumour development, progression and response to treatments. In particular, we discuss models of epithelial acini, multicellular spheroids, normal and tumour spheroids and organoids, and multi-component tissues. Our intent is to showcase how these in silico models can be applied to patient-specific data to assess which therapeutic strategies will be the most efficient. We also present the concept of virtual clinical trials that integrate standard-of-care patient data, medical imaging, organ-on-chip experiments and computational models to determine personalized medical treatment strategies.

In silico drug absorption tract: An agent-based biomimetic model for human oral drug absorption

Background

An agent-based modeling approach has been suggested as an alternative to traditional, equation-based modeling methods for describing oral drug absorption. It enables researchers to gain a better understanding of the pharmacokinetic (PK) mechanisms of a drug. This project demonstrates that a biomimetic agent-based model can adequately describe the absorption and disposition kinetics both of midazolam and clonazepam.

Methods

An agent-based biomimetic model, in silico drug absorption tract (ISDAT), was built to mimic oral drug absorption in humans. The model consisted of distinct spaces, membranes, and metabolic enzymes, and it was altogether representative of human physiology relating to oral drug absorption. Simulated experiments were run with the model, and the results were compared to the referent data from clinical equivalence trials. Acceptable similarity was verified by pre-specified criteria, which included 1) qualitative visual matching between the clinical and simulated concentration-time profiles, 2) quantitative similarity indices, namely, weighted root mean squared error (RMSE), and weighted mean absolute percentage error (MAPE) and 3) descriptive similarity which requires less than 25% difference between key PK parameters calculated by the clinical and the simulated concentration-time profiles. The model and its parameters were iteratively refined until all similarity criteria were met. Furthermore, simulated PK experiments were conducted to predict bioavailability (F). For better visualization, a graphical user interface for the model was developed and a video is available in Supporting Information.

Results

Simulation results satisfied all three levels of similarity criteria for both drugs. The weighted RMSE was 0.51 and 0.92, and the weighted MAPE was 5.99% and 8.43% for midazolam and clonazepam, respectively. Calculated PK parameter values, including area under the curve (AUC), peak plasma drug concentration (C_max), time to reach C_max (T_max), terminal elimination rate constant (Kel), terminal elimination half life (T_1/2), apparent oral clearance (CL/F), and apparent volume of distribution (V/F), were reasonable compared to the referent values. The predicted absolute oral bioavailability (F) was 44% for midazolam (literature reported value, 31–72%) and 93% (literature reported value, ≥ 90%) for clonazepam.

Conclusion

The ISDAT met all the pre-specified similarity criteria for both midazolam and clonazepam, and demonstrated its ability to describe absorption kinetics of both drugs. Therefore, the validated ISDAT can be a promising platform for further research into the use of similar in silico models for drug absorption kinetics.

Mistargeting of a truncated Na-K-2Cl cotransporter in epithelial cells

We recently reported the case of a young patient with multisystem failure carrying a de novo mutation in SLC12A2, the gene encoding the Na-K-2Cl cotransporter-1 (NKCC1). Heterologous expression studies in nonepithelial cells failed to demonstrate dominant-negative effects. In this study, we examined expression of the mutant cotransporter in epithelial cells. Using Madin-Darby canine kidney (MDCK) cells grown on glass coverslips, permeabilized support, and Matrigel, we show that the fluorescently tagged mutant cotransporter is expressed in cytoplasm and at the apical membrane and affects epithelium integrity. Expression of the mutant transporter at the apical membrane also results in the mislocalization of some of the wild-type transporter to the apical membrane. This mistargeting is specific to NKCC1 as the Na⁺-K⁺-ATPase remains localized on the basolateral membrane. To assess transporter localization in vivo, we created a mouse model using CRISPR/cas9 that reproduces the 11 bp deletion in exon 22 of Slc12a2. Although the mice do not display an overt phenotype, we show that the colon and salivary gland expresses wild-type NKCC1 abundantly at the apical pole, confirming the data obtained in cultured epithelial cells. Enough cotransporter must remain, however, on the basolateral membrane to participate in saliva secretion, as no significant decrease in saliva production was observed in the mutant mice.

Correlation between E-cadherin interactions, survivin expression, and apoptosis in MDCK and ts-Src MDCK cell culture models

Survivin, a member of inhibitor of apoptosis (IAP) protein family, is a multifunctional protein expressed in most cancers. In addition to inhibition of apoptosis, it regulates proliferation and promotes migration. Its presence and function in cells is strongly regulated via transcription factors, intracellular localization, and degradation. We analyzed the presence of survivin at protein level in various culture environments and under activation of Src tyrosine kinase in epithelial canine kidney MDCK cells in order to elucidate factors controlling survivin 'lifespan'. We used untransformed and temperature sensitive ts-Src MDCK cells as a model and forced them to grow in suspension (1D), in 2D on hard and soft surfaces and in soft 3D Matrigel environment with or without EGTA. In addition, we tested the effect of stressful conditions by cultivating the cells in the presence of an anti-cancer drug and a generator of reactive oxygen species (ROS), piperlongumine (PL) with or without an antioxidant, N-acetylcysteine (NAC). We could confirm that inhibition of apoptosis and simultaneous downregulation of survivin in MDCK cells required both intact cell-cell junctions, trans-interactions of E-cadherin and soft 3D matrix environment. In ts-Src-transformed MDCK cells, survivin was upregulated as soon as the cell-cell junctions were disintegrated. ROS generation with PL-induced cell death of ts-Src MDCK cells concomitantly with survivin downregulation. NAC rescued the ts-Src MDCK cells from ROS-induced apoptosis without upregulation of survivin resulting in a situation resembling untransformed MDCK cells in 3D environment and E-cadherin delineating the lateral cell walls.

Virtual-tissue computer simulations define the roles of cell adhesion and proliferation in the onset of kidney cystic disease

Virtual-tissue modeling is used to model emergent cyst growth in polycystic kidney disease. Model predictions, confirmed experimentally, show that decreased cell adhesion is necessary to produce stalk cysts, and loss of contact inhibition causes saccular cysts. Virtual-tissue modeling can be used to fully explore cell- and tissue-based behaviors.

In autosomal dominant polycystic kidney disease (ADPKD), cysts accumulate and progressively impair renal function. Mutations in PKD1 and PKD2 genes are causally linked to ADPKD, but how these mutations drive cell behaviors that underlie ADPKD pathogenesis is unknown. Human ADPKD cysts frequently express cadherin-8 (cad8), and expression of cad8 ectopically in vitro suffices to initiate cystogenesis. To explore cell behavioral mechanisms of cad8-driven cyst initiation, we developed a virtual-tissue computer model. Our simulations predicted that either reduced cell–cell adhesion or reduced contact inhibition of proliferation triggers cyst induction. To reproduce the full range of cyst morphologies observed in vivo, changes in both cell adhesion and proliferation are required. However, only loss-of-adhesion simulations produced morphologies matching in vitro cad8-induced cysts. Conversely, the saccular cysts described by others arise predominantly by decreased contact inhibition, that is, increased proliferation. In vitro experiments confirmed that cell–cell adhesion was reduced and proliferation was increased by ectopic cad8 expression. We conclude that adhesion loss due to cadherin type switching in ADPKD suffices to drive cystogenesis. Thus, control of cadherin type switching provides a new target for therapeutic intervention.

A cell-centered, agent-based framework that enables flexible environment granularities

BACKGROUND

Mechanistic explanations of cell-level phenomena typically adopt an observer perspective. Explanations developed from a cell's perspective may offer new insights. Agent-based models lend themselves to model from an individual perspective, and existing agent-based models generally utilize a regular lattice-based environment. A framework which utilizes a cell's perspective in an off-lattice environment could improve the overall understanding of biological phenomena.

RESULTS

We present an agent-based, discrete event framework, with a demonstrative focus on biomimetic agents. The framework was first developed in 2-dimensions and then extended, with a subset of behaviors, to 3-dimensions. The framework is expected to facilitate studies of more complex biological phenomena through exploitation of a dynamic Delaunay and Voronoi off-lattice environment. We used the framework to model biological cells and to specifically demonstrate basic biological cell behaviors in two- and three-dimensional space. Potential use cases are highlighted, suggesting the utility of the framework in various scenarios.

CONCLUSIONS

The framework presented in this manuscript expands on existing cell- and agent-centered methods by offering a new perspective in an off-lattice environment. As the demand for biomimetic models grows, the demand for new methods, such as the presented Delaunay and Voronoi framework, is expected to increase.

Towards anatomic scale agent-based modeling with a massively parallel spatially explicit general-purpose model of enteric tissue (SEGMEnT_HPC)

Perhaps the greatest challenge currently facing the biomedical research community is the ability to integrate highly detailed cellular and molecular mechanisms to represent clinical disease states as a pathway to engineer effective therapeutics. This is particularly evident in the representation of organ-level pathophysiology in terms of abnormal tissue structure, which, through histology, remains a mainstay in disease diagnosis and staging. As such, being able to generate anatomic scale simulations is a highly desirable goal. While computational limitations have previously constrained the size and scope of multi-scale computational models, advances in the capacity and availability of high-performance computing (HPC) resources have greatly expanded the ability of computational models of biological systems to achieve anatomic, clinically relevant scale. Diseases of the intestinal tract are exemplary examples of pathophysiological processes that manifest at multiple scales of spatial resolution, with structural abnormalities present at the microscopic, macroscopic and organ-levels. In this paper, we describe a novel, massively parallel computational model of the gut, the Spatially Explicitly General-purpose Model of Enteric Tissue_HPC (SEGMEnT_HPC), which extends an existing model of the gut epithelium, SEGMEnT, in order to create cell-for-cell anatomic scale simulations. We present an example implementation of SEGMEnT_HPC that simulates the pathogenesis of ileal pouchitis, and important clinical entity that affects patients following remedial surgery for ulcerative colitis.

The APC tumor suppressor is required for epithelial cell polarization and three-dimensional morphogenesis

The Adenomatous Polyposis Coli (APC) tumor suppressor has been previously implicated in the control of apical-basal polarity; yet, the consequence of APC loss-of-function in epithelial polarization and morphogenesis has not been characterized. To test the hypothesis that APC is required for the establishment of normal epithelial polarity and morphogenesis programs, we generated APC-knockdown epithelial cell lines. APC depletion resulted in loss of polarity and multi-layering on permeable supports, and enlarged, filled spheroids with disrupted polarity in 3D culture. Importantly, these effects of APC knockdown were independent of Wnt/β-catenin signaling, but were rescued with either full-length or a carboxy (c)-terminal segment of APC. Moreover, we identified a gene expression signature associated with APC knockdown that points to several candidates known to regulate cell-cell and cell-matrix communication. Analysis of epithelial tissues from mice and humans carrying heterozygous APC mutations further supports the importance of APC as a regulator of epithelial behavior and tissue architecture. These data also suggest that the initiation of epithelial-derived tumors as a result of APC mutation or gene silencing may be driven by loss of polarity and dysmorphogenesis.

Large-scale parameter studies of cell-based models of tissue morphogenesis using CompuCell3D or VirtualLeaf

Computational, cell-based models, such as the cellular Potts model (CPM), have become a widely used tool to study tissue formation. Most cell-based models mimic the physical properties of cells and their dynamic behavior, and generate images of the tissue that the cells form due to their collective behavior. Due to these intuitive parameters and output, cell-based models are often evaluated visually and the parameters are fine-tuned by hand. To get better insight into how in a cell-based model the microscopic scale (e.g., cell behavior, secreted molecular signals, and cell-ECM interactions) determines the macroscopic scale, we need to generate morphospaces and perform parameter sweeps, involving large numbers of individual simulations. This chapter describes a protocol and presents a set of scripts for automatically setting up, running, and evaluating large-scale parameter sweeps of cell-based models. We demonstrate the use of the protocol using a recent cellular Potts model of blood vessel formation model implemented in CompuCell3D. We show the versatility of the protocol by adapting it to an alternative cell-based modeling framework, VirtualLeaf.

In silico, experimental, mechanistic model for extended-release felodipine disposition exhibiting complex absorption and a highly variable food interaction

The objective of this study was to develop and explore new, in silico experimental methods for deciphering complex, highly variable absorption and food interaction pharmacokinetics observed for a modified-release drug product. Toward that aim, we constructed an executable software analog of study participants to whom product was administered orally. The analog is an object- and agent-oriented, discrete event system, which consists of grid spaces and event mechanisms that map abstractly to different physiological features and processes. Analog mechanisms were made sufficiently complicated to achieve prespecified similarity criteria. An equation-based gastrointestinal transit model with nonlinear mixed effects analysis provided a standard for comparison. Subject-specific parameterizations enabled each executed analog’s plasma profile to mimic features of the corresponding six individual pairs of subject plasma profiles. All achieved prespecified, quantitative similarity criteria, and outperformed the gastrointestinal transit model estimations. We observed important subject-specific interactions within the simulation and mechanistic differences between the two models. We hypothesize that mechanisms, events, and their causes occurring during simulations had counterparts within the food interaction study: they are working, evolvable, concrete theories of dynamic interactions occurring within individual subjects. The approach presented provides new, experimental strategies for unraveling the mechanistic basis of complex pharmacological interactions and observed variability.

An agent-based modeling framework linking inflammation and cancer using evolutionary principles: description of a generative hierarchy for the hallmarks of cancer and developing a bridge between mechanism and epidemiological data

Inflammation plays a critical role in the development and progression of cancer, evident in multiple patient populations manifesting increased, non-resolving inflammation, such as inflammatory bowel disease, viral hepatitis and obesity. Given the complexity of both the inflammatory response and the process of oncogenesis, we utilize principles from the field of Translational Systems Biology to bridge the gap between basic mechanistic knowledge and clinical/epidemiologic data by integrating inflammation and oncogenesis within an agent-based model, the Inflammation and Cancer Agent-based Model (ICABM). The ICABM utilizes two previously published and clinically/epidemiologically validated mechanistic models to demonstrate the role of an increased inflammatory milieu on oncogenesis. Development of the ICABM required the creation of a generative hierarchy of the basic hallmarks of cancer to provide a foundation to ground the plethora of molecular and pathway components currently being studied. The ordering schema emphasizes the essential role of a fitness/selection frame shift to sub-organismal evolution as a basic property of cancer, where the generation of genetic instability as a negative effect for multicellular eukaryotic organisms represents the restoration of genetic plasticity used as an adaptive strategy by colonies of prokaryotic unicellular organisms. Simulations with the ICABM demonstrate that inflammation provides a functional environmental context that drives the shift to sub-organismal evolution, where increasingly inflammatory environments led to increasingly damaged genomes in microtumors (tumors below clinical detection size) and cancers. The flexibility of this platform readily facilitates tailoring the ICABM to specific cancers, their associated mechanisms and available epidemiological data. One clinical example of an epidemiological finding that could be investigated with this platform is the increased incidence of triple negative breast cancers in the premenopausal African-American population, which has been identified as having up-regulated of markers of inflammation. The fundamental nature of the ICABM suggests its usefulness as a base platform upon which additional molecular detail could be added as needed.

Investigation of inflammation and tissue patterning in the gut using a Spatially Explicit General-purpose Model of Enteric Tissue (SEGMEnT)

The mucosa of the intestinal tract represents a finely tuned system where tissue structure strongly influences, and is turn influenced by, its function as both an absorptive surface and a defensive barrier. Mucosal architecture and histology plays a key role in the diagnosis, characterization and pathophysiology of a host of gastrointestinal diseases. Inflammation is a significant factor in the pathogenesis in many gastrointestinal diseases, and is perhaps the most clinically significant control factor governing the maintenance of the mucosal architecture by morphogenic pathways. We propose that appropriate characterization of the role of inflammation as a controller of enteric mucosal tissue patterning requires understanding the underlying cellular and molecular dynamics that determine the epithelial crypt-villus architecture across a range of conditions from health to disease. Towards this end we have developed the Spatially Explicit General-purpose Model of Enteric Tissue (SEGMEnT) to dynamically represent existing knowledge of the behavior of enteric epithelial tissue as influenced by inflammation with the ability to generate a variety of pathophysiological processes within a common platform and from a common knowledge base. In addition to reproducing healthy ileal mucosal dynamics as well as a series of morphogen knock-out/inhibition experiments, SEGMEnT provides insight into a range of clinically relevant cellular-molecular mechanisms, such as a putative role for Phosphotase and tensin homolog/phosphoinositide 3-kinase (PTEN/PI3K) as a key point of crosstalk between inflammation and morphogenesis, the protective role of enterocyte sloughing in enteric ischemia-reperfusion and chronic low level inflammation as a driver for colonic metaplasia. These results suggest that SEGMEnT can serve as an integrating platform for the study of inflammation in gastrointestinal disease.

Mutant p53 cooperates with knockdown of endogenous wild-type p53 to disrupt tubulogenesis in Madin-Darby canine kidney cells

Mutation of the p53 gene is the most common genetic alteration in human malignances and associated clinically with tumor progression and metastasis. To determine the effect of mutant p53 on epithelial differentiation, we developed three-dimensional culture (3-D) of Madin-Darby canine kidney (MDCK) cells. We found that parental MDCK cells undergo a series of morphological changes and form polarized and growth-arrested cysts with hollow lumen, which resembles branching tubules in vitro. We also found that upon knockdown of endogenous wild-type p53 (p53-KD), MDCK cells still form normal cysts in 3-D culture, indicating that p53-KD alone is not sufficient to disrupt cysts formation. However, we found that ectopic expression of mutant R163H (human equivalent R175H) or R261H (human equivalent R273H) in MDCK cells leads to disruption of cyst polarity and formation of invasive aggregates, which is further compounded by knockdown of endogenous wild-type p53. Consistently, we found that expression of E-cadherin, β-catenin, and epithelial-to-mesenchymal transition (EMT) transcription factors (Snail-1, Slug and Twist) is altered by mutant p53, which is also compounded by knockdown of wild-type p53. Moreover, the expression level of c-Met, the hepatocyte growth factor receptor and a key regulator of kidney cell tubulogenesis, is enhanced by combined knockdown of endogenous wild-type p53 and ectopic expression of mutant R163H or R261H but not by each individually. Together, our data suggest that upon inactivating mutation of the p53 gene, mutant p53 acquires its gain of function by altering morphogenesis and promoting cell migration and invasion in part by upregulating EMT and c-Met.

Polarity, cell division, and out-of-equilibrium dynamics control the growth of epithelial structures

The growth of a well-formed epithelial structure is governed by mechanical constraints, cellular apico-basal polarity, and spatially controlled cell division. Here we compared the predictions of a mathematical model of epithelial growth with the morphological analysis of 3D epithelial structures. In both in vitro cyst models and in developing epithelial structures in vivo, epithelial growth could take place close to or far from mechanical equilibrium, and was determined by the hierarchy of time-scales of cell division, cell-cell rearrangements, and lumen dynamics. Equilibrium properties could be inferred by the analysis of cell-cell contact topologies, and the nonequilibrium phenotype was altered by inhibiting ROCK activity. The occurrence of an aberrant multilumen phenotype was linked to fast nonequilibrium growth, even when geometric control of cell division was correctly enforced. We predicted and verified experimentally that slowing down cell division partially rescued a multilumen phenotype induced by altered polarity. These results improve our understanding of the development of epithelial organs and, ultimately, of carcinogenesis.

Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity

A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework—a dynamic knowledge repository—wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline. © 2013 Wiley Periodicals, Inc.

Examining the pathogenesis of breast cancer using a novel agent-based model of mammary ductal epithelium dynamics

The study of the pathogenesis of breast cancer is challenged by the long time-course of the disease process and the multi-factorial nature of generating oncogenic insults. The characterization of the longitudinal pathogenesis of malignant transformation from baseline normal breast duct epithelial dynamics may provide vital insight into the cascading systems failure that leads to breast cancer. To this end, extensive information on the baseline behavior of normal mammary epithelium and breast cancer oncogenesis was integrated into a computational model termed the Ductal Epithelium Agent-Based Model (DEABM). The DEABM is composed of computational agents that behave according to rules established from published cellular and molecular mechanisms concerning breast duct epithelial dynamics and oncogenesis. The DEABM implements DNA damage and repair, cell division, genetic inheritance and simulates the local tissue environment with hormone excretion and receptor signaling. Unrepaired DNA damage impacts the integrity of the genome within individual cells, including a set of eight representative oncogenes and tumor suppressors previously implicated in breast cancer, with subsequent consequences on successive generations of cells. The DEABM reproduced cellular population dynamics seen during the menstrual cycle and pregnancy, and demonstrated the oncogenic effect of known genetic factors associated with breast cancer, namely TP53 and Myc, in simulations spanning ∼40 years of simulated time. Simulations comparing normal to BRCA1-mutant breast tissue demonstrated rates of invasive cancer development similar to published epidemiologic data with respect to both cumulative incidence over time and estrogen-receptor status. Investigation of the modeling of ERα-positive (ER+) tumorigenesis led to a novel hypothesis implicating the transcription factor and tumor suppressor RUNX3. These data suggest that the DEABM can serve as a potentially valuable framework to augment the traditional investigatory workflow for future hypothesis generation and testing of the mechanisms of breast cancer oncogenesis.

Individualized, discrete event, simulations provide insight into inter- and intra-subject variability of extended-release, drug products

OBJECTIVE

Develop and validate particular, concrete, and abstract yet plausible in silico mechanistic explanations for large intra- and interindividual variability observed for eleven bioequivalence study participants. Do so in the face of considerable uncertainty about mechanisms.

METHODS

We constructed an object-oriented, discrete event model called subject (we use small caps to distinguish computational objects from their biological counterparts). It maps abstractly to a dissolution test system and study subject to whom product was administered orally. A subject comprises four interconnected grid spaces and event mechanisms that map to different physiological features and processes. Drugs move within and between spaces. We followed an established, Iterative Refinement Protocol. Individualized mechanisms were made sufficiently complicated to achieve prespecified Similarity Criteria, but no more so. Within subjects, the dissolution space is linked to both a product-subject Interaction Space and the GI tract. The GI tract and Interaction Space connect to plasma, from which drug is eliminated.

RESULTS

We discovered parameterizations that enabled the eleven subject simulation results to achieve the most stringent Similarity Criteria. Simulated profiles closely resembled those with normal, odd, and double peaks. We observed important subject-by-formulation interactions within subjects.

CONCLUSION

We hypothesize that there were interactions within bioequivalence study participants corresponding to the subject-by-formulation interactions within subjects. Further progress requires methods to transition currently abstract subject mechanisms iteratively and parsimoniously to be more physiologically realistic. As that objective is achieved, the approach presented is expected to become beneficial to drug development (e.g., controlled release) and to a reduction in the number of subjects needed per study plus faster regulatory review.

Creation of trophectoderm, the first epithelium, in mouse preimplantation development

Trophectoderm (TE) is the first cell type that emerges during development and plays pivotal roles in the viviparous mode of reproduction in placental mammals. TE adopts typical epithelium morphology to surround a fluid-filled cavity, whose expansion is critical for hatching and efficient interaction with the uterine endometrium for implantation. TE also differentiates into trophoblast cells to construct the placenta. This chapter is an overview of the cellular and molecular mechanisms that control the critical aspects of TE formation, namely, the formation of the blastocyst cavity, the expression of key transcription factors, and the roles of cell polarity in the specification of the TE lineage. Current gaps in our knowledge and challenging issues are also discussed that should be addressed in future investigations in order to further advance our understanding of the mechanisms of TE formation.

Relational grounding facilitates development of scientifically useful multiscale models

We review grounding issues that influence the scientific usefulness of any biomedical multiscale model (MSM). Groundings are the collection of units, dimensions, and/or objects to which a variable or model constituent refers. To date, models that primarily use continuous mathematics rely heavily on absolute grounding, whereas those that primarily use discrete software paradigms (e.g., object-oriented, agent-based, actor) typically employ relational grounding. We review grounding issues and identify strategies to address them. We maintain that grounding issues should be addressed at the start of any MSM project and should be reevaluated throughout the model development process. We make the following points. Grounding decisions influence model flexibility, adaptability, and thus reusability. Grounding choices should be influenced by measures, uncertainty, system information, and the nature of available validation data. Absolute grounding complicates the process of combining models to form larger models unless all are grounded absolutely. Relational grounding facilitates referent knowledge embodiment within computational mechanisms but requires separate model-to-referent mappings. Absolute grounding can simplify integration by forcing common units and, hence, a common integration target, but context change may require model reengineering. Relational grounding enables synthesis of large, composite (multi-module) models that can be robust to context changes. Because biological components have varying degrees of autonomy, corresponding components in MSMs need to do the same. Relational grounding facilitates achieving such autonomy. Biomimetic analogues designed to facilitate translational research and development must have long lifecycles. Exploring mechanisms of normal-to-disease transition requires model components that are grounded relationally. Multi-paradigm modeling requires both hyperspatial and relational grounding.

Enabling clearance predictions to emerge from in silico actions of quasi-autonomous hepatocyte components

We demonstrate the feasibility of using in silico hepatocyte cultures (ISHCs) to provide predictions of the intrinsic clearance (CL) of compounds in hepatocyte cultures. We compare results with predictions obtained using a multiple linear regression method. Our expectation is that the method can be extended to predict in vivo clearance of new compounds in humans. Within ISHCs, mobile "compounds" carry information describing referent compound properties. We used an iterative refinement protocol for ISHC refinement and development of parameterization methods. Quasi-autonomous "hepatocytes" and their components (including "transporters" and "enzymes") use a small, event-specific subset of compound properties to decide how to interact with encountered compounds each simulation cycle. The probability of occurrence for each event is specified by a rule that uses a subset of compound properties known to influence that event in vitro. ISHC experiments mimic in vitro counterparts. In silico clearance is measured the same as in vitro clearance and is used to predict a corresponding CL value. For 39 of 73 compounds having calculated CL S.D.s, 79% of ISHC predictions and 23% of regression predictions were within CL ± 2 S.D. For all 73 compounds, 38% of ISHC predictions and 32% of regression predictions were within a factor of 2 of the referent CL values. ISHC details during simulations stand as a mechanistic hypothesis of how clearance phenomena emerge during in vitro experiments.