Dynamic intercellular transport modulates the spatial patterning of differentiation during early neural commitment

EZH2 inhibition sensitizes retinoic acid-driven senescence in synovial sarcoma

Synovial sarcoma (SS) is driven by a unique t(18;X) chromosomal translocation resulting in expression of the SS18-SSX fusion oncoprotein, a transcriptional regulator with both activating and repressing functions. However, the manner in which SS18-SSX contributes to the development of SS is not entirely known. Here, we show that SS18-SSX drives the expression of Preferentially Expressed Antigen in Melanoma (PRAME), which is highly expressed in SS but whose function remains poorly understood. The fusion protein directly binds and activates the PRAME promoter and we found that expression of SS18-SSX and PRAME are positively correlated. We provide evidence that PRAME modulates retinoic acid (RA) signaling, forming a ternary complex with the RA receptor α (RARα) and the Enhancer of Zeste Homolog 2 (EZH2). Knockdown of PRAME suppressed the response to all-trans retinoic acid (ATRA) supporting PRAME's role in modulating RA-signaling. Notably, we demonstrate that combined pharmacological inhibition of EZH2 and treatment with ATRA reconstituted RA signaling followed by reduced proliferation and induction of cellular senescence. In conclusion, our data provides new insights on the role of the SS18-SSX fusion protein in regulation of PRAME expression and RA signaling, highlighting the therapeutic potential of disrupting the RARα-PRAME-EZH2 complex in SS. Schematic presentation of the proposed model. A The RARα-PRAME-EZH2 ternary complex in SS. The fusion SS18-SSX oncoprotein binds to the PRAME promoter and activates its expression. PRAME in turn interacts with RARα-RXR heterodimers as well as with EZH2, and the complex binds to retinoic acid response elements (RAREs) in the DNA. This results in transcriptional repression of retinoic acid (RA) responsive genes and thus inhibition of RA-signaling, allowing tumor cell proliferation. B Therapeutic strategy. Treatment with an EZH2 inhibitor, such as GSK343, or activation of RAR receptors via all-trans retinoic acid (ATRA), disrupts the RARα-PRAME-EZH2 ternary complex and restores RA-signaling. Exposure to GSK343 or ATRA results in inhibition of cell proliferation and induction of cellular senescence, where GSK343 shows a dominant effect. The Figure was created with Biorender.com.

Multicellular adaptation to electrophysiological perturbations analyzed by deterministic and stochastic bioelectrical models

Cells can compensate a disruptive change in one ion channel by compensatory changes in other channels. We have simulated the adaptation of a multicellular aggregate of non-excitable cells to the electrophysiological perturbation produced by the external blocking of a cation channel. In the biophysical model employed, we consider that this blocking provokes a cell depolarization that opens a voltage-gated calcium channel, thus allowing toxic Ca2+ levels. The cell adaptation to this externally-induced perturbation is ascribed to the multiplicity of channels available to keep the cell membrane potential within a physiological window. We propose that the cell depolarization provokes the upregulated expression of a compensatory channel protein that resets the cell potential to the correct polarized value, which prevents the calcium entry. To this end, we use two different simulation algorithms based on deterministic and stochastic methods. The simulations suggest that because of the local correlations coupling the cell potential to transcription, short-term bioelectrical perturbations can trigger long-term biochemical adaptations to novel stressors in multicellular aggregates. Previous experimental data on planarian flatworms' adaptation to a barium-containing environment is also discussed.

Oscillatory phenomena in electrophysiological networks: The coupling between cell bioelectricity and transcription

Morphogenetic regulation during embryogenesis and regeneration rely on information transfer and coordination between different regions. Here, we explore theoretically the coupling between bioelectrical and transcriptional oscillations at the individual cell and multicellular levels. The simulations, based on a set of ion channels and intercellular gap junctions, show that bioelectrical and transcriptional waves can electrophysiologically couple distant regions of a model network in phase and antiphase oscillatory states that include synchronization phenomena. In this way, different multicellular regionalizations can be encoded by cell potentials that oscillate between depolarized and polarized states, thus allowing a spatio-temporal coding. Because the electric potential patterns characteristic of development and regeneration are correlated with the spatial distributions of signaling ions and molecules, bioelectricity can act as a template for slow biochemical signals following a hierarchy of experimental times. In particular, bioelectrical gradients that couple cell potentials to transcription rates give to each single cell a rough idea of its location in the multicellular ensemble, thus controlling local differentiation processes that switch on and off crucial parts of the genome.

Identification of Distinct, Quantitative Pattern Classes from Emergent Tissue-Scale hiPSC Bioelectric Properties

Bioelectric signals possess the ability to robustly control and manipulate patterning during embryogenesis and tissue-level regeneration. Endogenous local and global electric fields function as a spatial 'pre-pattern', controlling cell fates and tissue-scale anatomical boundaries; however, the mechanisms facilitating these robust multiscale outcomes are poorly characterized. Computational modeling addresses the need to predict in vitro patterning behavior and further elucidate the roles of cellular bioelectric signaling components in patterning outcomes. Here, we modified a previously designed image pattern recognition algorithm to distinguish unique spatial features of simulated non-excitable bioelectric patterns under distinct cell culture conditions. This algorithm was applied to comparisons between simulated patterns and experimental microscopy images of membrane potential (Vmem) across cultured human iPSC colonies. Furthermore, we extended the prediction to a novel co-culture condition in which cell sub-populations possessing different ionic fluxes were simulated; the defining spatial features were recapitulated in vitro with genetically modified colonies. These results collectively inform strategies for modeling multiscale spatial characteristics that emerge in multicellular systems, characterizing the molecular contributions to heterogeneity of membrane potential in non-excitable cells, and enabling downstream engineered bioelectrical tissue design.

The collective dynamics of frustrated biological neuron networks

To maintain normal functionality, it is necessary for a multicellular organism to generate robust responses to external temporal signals. However, the underlying mechanisms to coordinate the collective dynamics of cells remain poorly understood. Here we study the calcium activity of micropatterned biological neuron networks excited by periodic ATP stimuli. Combining quantitative experiments, physical and biological manipulation of cells, as well as mathematical modeling, we show that isolated cells in a network become more synchronized at longer period of stimuli through noise cancellation. However, slowly varying external signal also increases gap junction coupling between connected nodes in the network; and gap junction mediated communication may destroy network synchronization due to special nonlinear bifurcations exhibited by the excitable dynamics of neuronal cells. Based on our results, we propose that a biological neuron network supported by gap junctional communication encodes external temporal signals in its network dynamics. A sparely connected network approaches synchronization as input signal slows down, whereas a highly connected network enters dynamic frustration in the same situation.

Correcting instructive electric potential patterns in multicellular systems: External actions and endogenous processes

BACKGROUND

Transmembrane electrical potential differences in cells modulate the spatio-temporal distribution of signaling ions and molecules that are instructive for downstream signaling pathways in multicellular systems. The local coupling between bioelectricity and protein transcription patterns allows dynamic subsystems (modules) of cells that share the same bioelectrical state to show similar biochemical downstream processes.

METHODS

We simulate theoretically how the integration-segregation pattern formed by the different multicellular modules that define a biosystem can be controlled by multicellular potentials. To this end, we couple together the model equations of the bioelectrical network to those of the genetic network.

RESULTS

The coupling provided by the intercellular junctions and the external microenvironment allows the restoration of the target bioelectrical pattern by changing the transcription rate of specific ion channels, the post-translational blocking of these channels, and changes in the environmental ionic concentrations.

CONCLUSIONS

The simulations show that the single-cell feedback between bioelectrical and transcriptional processes, together with the coupling provided by the intercellular junctions and the environment, can correct large-scale patterns by means of suitable external actions.

GENERAL SIGNIFICANCE

This study provides a theoretical advancement in the understanding of how the multicellular bioelectric coupling may guide repolarizing interventions for regenerating a tissue, with potential implications in biomedicine.

Transplantation of fragments from different planaria: A bioelectrical model for head regeneration

Head-tail planaria morphologies are influenced by the electric potential differences across the animal's primary axis, as evidenced e.g. by voltage-sensitive dyes and functional experiments that create permanent lines of 2-headed but genetically wild-type animals. However, bioelectrical and biochemical models that make predictions on what would happen in the case of spatial chimeras made by tissue transplantation from different planaria (different species and head shapes) are lacking. Here, we use a bioelectrical model to qualitatively describe the effects of tissue transplantation on the shape of the regenerated head. To this end, we assume that the cells may have distinct sets of ion channels and ascribe the system outcome to the axial distributions of average cell potentials over morphologically relevant regions. Our rationale is that the distributions of signaling ions and molecules are spatially coupled with multicellular electric potentials. Thus, long-time downstream transcriptional events should be triggered by short-time bioelectrical processes. We show that relatively small differences between the ion channel characteristics of the cells could eventually give noticeable changes in the electric potential profiles and the expected morphological deviations, which suggests that small but timely bioelectrical actions may have significant morphological effects. Our approach is based on the observed relationships between bioelectrical regionalization and biochemical gradients in body-plan studies. Such models are relevant to regenerative, developmental, and cancer biology in which cells with distinct properties and morphogenetic target states confront each other in the same tissue.

Engineering multicellular living systems-a Keystone Symposia report

The ability to engineer complex multicellular systems has enormous potential to inform our understanding of biological processes and disease and alter the drug development process. Engineering living systems to emulate natural processes or to incorporate new functions relies on a detailed understanding of the biochemical, mechanical, and other cues between cells and between cells and their environment that result in the coordinated action of multicellular systems. On April 3-6, 2022, experts in the field met at the Keystone symposium "Engineering Multicellular Living Systems" to discuss recent advances in understanding how cells cooperate within a multicellular system, as well as recent efforts to engineer systems like organ-on-a-chip models, biological robots, and organoids. Given the similarities and common themes, this meeting was held in conjunction with the symposium "Organoids as Tools for Fundamental Discovery and Translation".

Connexins Signatures of the Neurovascular Unit and Their Physio-Pathological Functions

Central nervous system (CNS) homeostasis is closely linked to the delicate balance of the microenvironment in which different cellular components of the neurovascular unit (NVU) coexist. Intercellular communication plays a pivotal role in exchanges of signaling molecules and mediators essential for survival functions, as well as in the removal of disturbing elements that can lead to related pathologies. The specific signatures of connexins (Cxs), proteins which form either gap junctions (GJs) or hemichannels (HCs), represent the biological substrate of the pathophysiological balance. Connexin 43 (Cx43) is undoubtedly one of the most important factors in glia–neuro–vascular crosstalk. Herein, Cxs signatures of every NVU component are highlighted and their critical influence on functional processes in healthy and pathological conditions of nervous microenvironment is reviewed.

Protease-controlled secretion and display of intercellular signals

To program intercellular communication for biomedicine, it is crucial to regulate the secretion and surface display of signaling proteins. If such regulations are at the protein level, there are additional advantages, including compact delivery and direct interactions with endogenous signaling pathways. Here we create a modular, generalizable design called Retained Endoplasmic Cleavable Secretion (RELEASE), with engineered proteins retained in the endoplasmic reticulum and displayed/secreted in response to specific proteases. The design allows functional regulation of multiple synthetic and natural proteins by synthetic protease circuits to realize diverse signal processing capabilities, including logic operation and threshold tuning. By linking RELEASE to additional sensing and processing circuits, we can achieve elevated protein secretion in response to "undruggable" oncogene KRAS mutants. RELEASE should enable the local, programmable delivery of intercellular cues for a broad variety of fields such as neurobiology, cancer immunotherapy and cell transplantation.

Cell Systems Bioelectricity: How Different Intercellular Gap Junctions Could Regionalize a Multicellular Aggregate

Electric potential distributions can act as instructive pre-patterns for development, regeneration, and tumorigenesis in cell systems. The biophysical states influence transcription, proliferation, cell shape, migration, and differentiation through biochemical and biomechanical downstream transduction processes. A major knowledge gap is the origin of spatial patterns in vivo, and their relationship to the ion channels and the electrical synapses known as gap junctions. Understanding this is critical for basic evolutionary developmental biology as well as for regenerative medicine. We computationally show that cells may express connexin proteins with different voltage-gated gap junction conductances as a way to maintain multicellular regions at distinct membrane potentials. We show that increasing the multicellular connectivity via enhanced junction function does not always contribute to the bioelectrical normalization of abnormally depolarized multicellular patches. From a purely electrical junction view, this result suggests that the reduction rather than the increase of specific connexin levels can also be a suitable bioelectrical approach in some cases and time stages. We offer a minimum model that incorporates effective conductances ultimately related to specific ion channel and junction proteins that are amenable to external regulation. We suggest that the bioelectrical patterns and their encoded instructive information can be externally modulated by acting on the mean fields of cell systems, a complementary approach to that of acting on the molecular characteristics of individual cells. We believe that despite the limitations of a biophysically focused model, our approach can offer useful qualitative insights into the collective dynamics of cell system bioelectricity.

Generation of 2.5D lung bud organoids from human induced pluripotent stem cells

Human induced pluripotent stem cells (hiPSCs) are a promising cell source to generate the patient-specific lung organoid given their superior differentiation potential. However, the current 3D cell culture approach is tedious and time-consuming with a low success rate and high batch-to-batch variability. Here, we explored the establishment of lung bud organoids by systematically adjusting the initial confluence levels and homogeneity of cell distribution. The efficiency of single cell seeding and clump seeding was compared. Instead of the traditional 3D culture, we established a 2.5D organoid culture to enable the direct monitoring of the internal structure via microscopy. It was found that the cell confluence and distribution prior to induction were two key parameters, which strongly affected hiPSC differentiation trajectories. Lung bud organoids with positive expression of NKX 2.1, in a single-cell seeding group with homogeneously distributed hiPSCs at 70% confluence (SC_70%_hom) or a clump seeding group with heterogeneously distributed cells at 90% confluence (CL_90%_het), can be observed as early as 9 days post induction. These results suggest that a successful lung bud organoid formation with single-cell seeding of hiPSCs requires a moderate confluence and homogeneous distribution of cells, while high confluence would be a prominent factor to promote the lung organoid formation when seeding hiPSCs as clumps. 2.5D organoids generated with defined culture conditions could become a simple, efficient, and valuable tool facilitating drug screening, disease modeling and personalized medicine.

Morphology changes induced by intercellular gap junction blocking: A reaction-diffusion mechanism

Complex anatomical form is regulated in part by endogenous physiological communication between cells; however, the dynamics by which gap junctional (GJ) states across tissues regulate morphology are still poorly understood. We employed a biophysical modeling approach combining different signaling molecules (morphogens) to qualitatively describe the anteroposterior and lateral morphology changes in model multicellular systems due to intercellular GJ blockade. The model is based on two assumptions for blocking-induced patterning: (i) the local concentrations of two small antagonistic morphogens diffusing through the GJs along the axial direction, together with that of an independent, uncoupled morphogen concentration along an orthogonal direction, constitute the instructive patterns that modulate the morphological outcomes, and (ii) the addition of an external agent partially blocks the intercellular GJs between neighboring cells and modifies thus the establishment of these patterns. As an illustrative example, we study how the different connectivity and morphogen patterns obtained in presence of a GJ blocker can give rise to novel head morphologies in regenerating planaria. We note that the ability of GJs to regulate the permeability of morphogens post-translationally suggests a mechanism by which different anatomies can be produced from the same genome without the modification of gene-regulatory networks. Conceptually, our model biosystem constitutes a reaction-diffusion information processing mechanism that allows reprogramming of biological morphologies through the external manipulation of the intercellular GJs and the resulting changes in instructive biochemical signals.

A bioelectric model of carcinogenesis, including propagation of cell membrane depolarization and reversal therapies

As the main theory of carcinogenesis, the Somatic Mutation Theory, increasingly presents difficulties to explain some experimental observations, different theories are being proposed. A major alternative approach is the Tissue Organization Field Theory, which explains cancer origin as a tissue regulation disease instead of having a mainly cellular origin. This work fits in the latter hypothesis, proposing the bioelectric field, in particular the cell membrane polarization state, and ionic exchange through ion channels and gap junctions, as an important mechanism of cell communication and tissue organization and regulation. Taking into account recent experimental results and proposed bioelectric models, a computational model of cancer initiation was developed, including the propagation of a cell depolarization wave in the tissue under consideration. Cell depolarization leads to a change in its state, with the activation and deactivation of several regulation pathways, increasing cell proliferation and motility, changing its epigenetic state to a more stem cell-like behavior without the requirement of genomic mutation. The intercellular communication via gap junctions leads, in certain circumstances, to a bioelectric state propagation to neighbor cells, in a chain-like reaction, till an electric discontinuity is reached. However, this is a reversible process, and it was shown experimentally that, by implementing a therapy targeted on cell ion exchange channels, it is possible to reverse the state and repolarize cells. This mechanism can be an important alternative way in cancer prevention, diagnosis and therapy, and new experiments are proposed to test the presented hypothesis.

Effects of vitamin A and retinoic acid on mouse embryonic stem cells and their differentiating progeny

Embryonic development is controlled by retinoids, and one approach that has been used to investigate the mechanisms for retinoid actions in developmental processes has been to study the effects of adding retinoids to cultures of pluripotent embryonic stem cells (ESC). To date, most in vitro retinoid research has been directed at deciphering the actions of all-trans retinoic acid (atRA). atRA is a derivative of all-trans retinol (a.k.a. vitamin A, VA), which mammals can generate via an enzyme-catalyzed pathway. atRA's effects on development result from its (1) activation of receptor complexes (RARs and RXRs) in the nucleus which then bind to and activate RA response elements (RAREs) in genes and (2) its interactions with processes that are initiated in the cytoplasm. While much work has focused on the impact of atRA on cell differentiation, VA, itself, has been shown to exert effects on the maintenance of ESC identity that are not dependent upon classic RA-signaling pathways. In this chapter, we present results from our laboratory and others using well-documented approaches for investigating the effects of retinoids on the differentiation of ESC in vitro and introduce a novel method that uses chemically-defined growth conditions. The merits of each approach are discussed.

Deep neural net tracking of human pluripotent stem cells reveals intrinsic behaviors directing morphogenesis

Lineage tracing is a powerful tool in developmental biology to interrogate the evolution of tissue formation, but the dense, three-dimensional nature of tissue limits the assembly of individual cell trajectories into complete reconstructions of development. Human induced pluripotent stem cells (hiPSCs) can recapitulate aspects of developmental processes, providing an in vitro platform to assess the dynamic collective behaviors directing tissue morphogenesis. Here, we trained an ensemble of neural networks to track individual hiPSCs in time-lapse microscopy, generating longitudinal measures of cell and cellular neighborhood properties on timescales from minutes to days. Our analysis reveals that, while individual cell parameters are not strongly affected by pluripotency maintenance conditions or morphogenic cues, regional changes in cell behavior predict cell fate and colony organization. By generating complete multicellular reconstructions of hiPSC behavior, our tracking pipeline enables fine-grained understanding of morphogenesis by elucidating the role of regional behavior in early tissue formation.

Community effects allow bioelectrical reprogramming of cell membrane potentials in multicellular aggregates: Model simulations

Bioelectrical patterns are established by spatiotemporal correlations of cell membrane potentials at the multicellular level, being crucial to development, regeneration, and tumorigenesis. We have conducted multicellular simulations on bioelectrical community effects and intercellular coupling in multicellular aggregates. The simulations aim at establishing under which conditions a local heterogeneity consisting of a small patch of cells can be stabilized against a large aggregate of surrounding identical cells which are in a different bioelectrical state. In this way, instructive bioelectrical information can be persistently encoded in spatiotemporal patterns of separated domains with different cell polarization states. The multicellular community effects obtained are regulated both at the single-cell and intercellular levels, and emerge from a delicate balance between the degrees of intercellular coupling in: (i) the small patch, (ii) the surrounding bulk, and (iii) the interface that separates these two regions. The model is experimentally motivated and consists of two generic voltage-gated ion channels that attempt to establish the depolarized and polarized cell states together with coupling conductances whose individual and intercellular different states permit a dynamic multicellular connectivity. The simulations suggest that community effects may allow the reprogramming of single-cell bioelectrical states, in agreement with recent experimental data. A better understanding of the resulting electrical regionalization can assist the electroceutical correction of abnormally depolarized regions initiated in the bulk of normal tissues as well as suggest new biophysical mechanisms for the establishment of target patterns in multicellular engineering.

Visualization and quantification of dynamic intercellular coupling in human embryonic stem cells using single cell sonoporation

Gap junctions (GJs), which are proteinaceous channels, couple adjacent cells by permitting direct exchange of intracellular molecules with low molecular weights. GJ intercellular communication (GJIC) plays a critical role in regulating behaviors of human embryonic stem cells (hESCs), affecting their proliferation and differentiation. Here we report a novel use of sonoporation that enables single cell intracellular dye loading and dynamic visualization/quantification of GJIC in hESC colonies. By applying a short ultrasound pulse to excite single microbubbles tethered to cell membranes, a transient pore on the cell membrane (sonoporation) is generated which allows intracellular loading of dye molecules and influx of Ca²⁺ into single hESCs. We employ live imaging for continuous visualization of intercellular dye transfer and Ca²⁺ diffusion in hESC colonies. We quantify cell–cell permeability based on dye diffusion using mass transport models. Our results reveal heterogeneous intercellular connectivity and a variety of spatiotemporal characteristics of intercellular Ca²⁺ waves in hESC colonies induced by sonoporation of single cells.

Using Acoustic Fields to Fabricate ECM-Based Biomaterials for Regenerative Medicine Applications

Ultrasound is emerging as a promising tool for both characterizing and fabricating engineered biomaterials. Ultrasound-based technologies offer a diverse toolbox with outstanding capacity for optimization and customization within a variety of therapeutic contexts, including improved extracellular matrix-based materials for regenerative medicine applications. Non-invasive ultrasound fabrication tools include the use of thermal and mechanical effects of acoustic waves to modify the structure and function of extracellular matrix scaffolds both directly, and indirectly via biochemical and cellular mediators. Materials derived from components of native extracellular matrix are an essential component of engineered biomaterials designed to stimulate cell and tissue functions and repair or replace injured tissues. Thus, continued investigations into biological and acoustic mechanisms by which ultrasound can be used to manipulate extracellular matrix components within three-dimensional hydrogels hold much potential to enable the production of improved biomaterials for clinical and research applications.

A Co-registration Pipeline for Multimodal MALDI and Confocal Imaging Analysis of Stem Cell Colonies

Multimodal mass spectrometry imaging (MSI) data presents unique big data challenges in handling and analysis. Here, we present a pipeline for co-registering matrix-assisted laser desorption/ionization MSI and confocal immunofluorescence imaging data for extracting single-cell metabolite signatures. We further describe methods and introduce software for the simultaneous analysis of these concatenated data sets, which are designed to establish a connection between cell traits of interest (shape metrics, position within sample) and the cells' own metabolic signatures.

Modeling somatic computation with non-neural bioelectric networks

The field of basal cognition seeks to understand how adaptive, context-specific behavior occurs in non-neural biological systems. Embryogenesis and regeneration require plasticity in many tissue types to achieve structural and functional goals in diverse circumstances. Thus, advances in both evolutionary cell biology and regenerative medicine require an understanding of how non-neural tissues could process information. Neurons evolved from ancient cell types that used bioelectric signaling to perform computation. However, it has not been shown whether or how non-neural bioelectric cell networks can support computation. We generalize connectionist methods to non-neural tissue architectures, showing that a minimal non-neural Bio-Electric Network (BEN) model that utilizes the general principles of bioelectricity (electrodiffusion and gating) can compute. We characterize BEN behaviors ranging from elementary logic gates to pattern detectors, using both fixed and transient inputs to recapitulate various biological scenarios. We characterize the mechanisms of such networks using dynamical-systems and information-theory tools, demonstrating that logic can manifest in bidirectional, continuous, and relatively slow bioelectrical systems, complementing conventional neural-centric architectures. Our results reveal a variety of non-neural decision-making processes as manifestations of general cellular biophysical mechanisms and suggest novel bioengineering approaches to construct functional tissues for regenerative medicine and synthetic biology as well as new machine learning architectures.

Automated Design of Pluripotent Stem Cell Self-Organization

Human pluripotent stem cells (hPSCs) have the intrinsic ability to self-organize into complex multicellular organoids that recapitulate many aspects of tissue development. However, robustly directing morphogenesis of hPSC-derived organoids requires novel approaches to accurately control self-directed pattern formation. Here, we combined genetic engineering with computational modeling, machine learning, and mathematical pattern optimization to create a data-driven approach to control hPSC self-organization by knock down of genes previously shown to affect stem cell colony organization, CDH1 and ROCK1. Computational replication of the in vitro system in silico using an extended cellular Potts model enabled machine learning-driven optimization of parameters that yielded emergence of desired patterns. Furthermore, in vitro the predicted experimental parameters quantitatively recapitulated the in silico patterns. These results demonstrate that morphogenic dynamics can be accurately predicted through model-driven exploration of hPSC behaviors via machine learning, thereby enabling spatial control of multicellular patterning to engineer human organoids and tissues. A record of this paper's Transparent Peer Review process is included in the Supplemental Information.

Multi-cellular engineered living systems: building a community around responsible research on emergence

Ranging from miniaturized biological robots to organoids, multi-cellular engineered living systems (M-CELS) pose complex ethical and societal challenges. Some of these challenges, such as how to best distribute risks and benefits, are likely to arise in the development of any new technology. Other challenges arise specifically because of the particular characteristics of M-CELS. For example, as an engineered living system becomes increasingly complex, it may provoke societal debate about its moral considerability, perhaps necessitating protection from harm or recognition of positive moral and legal rights, particularly if derived from cells of human origin. The use of emergence-based principles in M-CELS development may also create unique challenges, making the technology difficult to fully control or predict in the laboratory as well as in applied medical or environmental settings. In response to these challenges, we argue that the M-CELS community has an obligation to systematically address the ethical and societal aspects of research and to seek input from and accountability to a broad range of stakeholders and publics. As a newly developing field, M-CELS has a significant opportunity to integrate ethically responsible norms and standards into its research and development practices from the start. With the aim of seizing this opportunity, we identify two general kinds of salient ethical issues arising from M-CELS research, and then present a set of commitments to and strategies for addressing these issues. If adopted, these commitments and strategies would help define M-CELS as not only an innovative field, but also as a model for responsible research and engineering.

Agent-based modeling of morphogenetic systems: Advantages and challenges

The complexity of morphogenesis poses a fundamental challenge to understanding the mechanisms governing the formation of biological patterns and structures. Over the past century, numerous processes have been identified as critically contributing to morphogenetic events, but the interplay between the various components and aspects of pattern formation have been much harder to grasp. The combination of traditional biology with mathematical and computational methods has had a profound effect on our current understanding of morphogenesis and led to significant insights and advancements in the field. In particular, the theoretical concepts of reaction–diffusion systems and positional information, proposed by Alan Turing and Lewis Wolpert, respectively, dramatically influenced our general view of morphogenesis, although typically in isolation from one another. In recent years, agent-based modeling has been emerging as a consolidation and implementation of the two theories within a single framework. Agent-based models (ABMs) are unique in their ability to integrate combinations of heterogeneous processes and investigate their respective dynamics, especially in the context of spatial phenomena. In this review, we highlight the benefits and technical challenges associated with ABMs as tools for examining morphogenetic events. These models display unparalleled flexibility for studying various morphogenetic phenomena at multiple levels and have the important advantage of informing future experimental work, including the targeted engineering of tissues and organs.

Algorithmic and Stochastic Representations of Gene Regulatory Networks and Protein-Protein Interactions

BACKGROUND

Latest studies reveal the importance of Protein-Protein interactions on physiologic functions and biological structures. Several stochastic and algorithmic methods have been published until now, for the modeling of the complex nature of the biological systems.

OBJECTIVE

Biological Networks computational modeling is still a challenging task. The formulation of the complex cellular interactions is a research field of great interest. In this review paper, several computational methods for the modeling of GRN and PPI are presented analytically.

METHODS

Several well-known GRN and PPI models are presented and discussed in this review study such as: Graphs representation, Boolean Networks, Generalized Logical Networks, Bayesian Networks, Relevance Networks, Graphical Gaussian models, Weight Matrices, Reverse Engineering Approach, Evolutionary Algorithms, Forward Modeling Approach, Deterministic models, Static models, Hybrid models, Stochastic models, Petri Nets, BioAmbients calculus and Differential Equations.

RESULTS

GRN and PPI methods have been already applied in various clinical processes with potential positive results, establishing promising diagnostic tools.

CONCLUSION

In literature many stochastic algorithms are focused in the simulation, analysis and visualization of the various biological networks and their dynamics interactions, which are referred and described in depth in this review paper.