scholarly journals Agent-Based Model of Multicellular Spheroid Pattern Formation Driven by Synthetic Cell Adhesion Signaling Circuits

2021 ◽  
Author(s):  
Nikita Sivakumar ◽  
Helen V. Warner ◽  
Shayn M. Peirce ◽  
Matthew J. Lazzara

Dynamically activated differential adhesion within cell populations enables the emergence of unique patterns in heterogeneous multicellular systems. This process has previously been explored using synthetically engineered heterogenous multicell spheroid systems in which cell subpopulations engage in bidirectional intercellular signaling to regulate the expression of different cadherins. While engineered cell systems provide excellent experimental tools to observe pattern formation in cell populations, computational models may be leveraged to explore the key parameters that drive the emergence of different patterns more systematically. Here, we developed and validated two- and three-dimensional agent-based models (ABMs) of spheroid patterning for cells engineered with a bidirectional signaling circuit regulating N- and P-cadherin expression. The model was used to predict how varying initial cell seeding, cadherin induction probabilities, or homotypic adhesion strengths between cells leads to different spheroid patterns, and unsupervised machine learning techniques were used to map system parameters to unique spheroid patterns. The model was also used as a tool to design new synthetic cell signaling circuits based on a desired final multicell pattern.

2020 ◽  
Vol 27 ◽  
Author(s):  
Gabriela Bitencourt-Ferreira ◽  
Camila Rizzotto ◽  
Walter Filgueira de Azevedo Junior

Background: Analysis of atomic coordinates of protein-ligand complexes can provide three-dimensional data to generate computational models to evaluate binding affinity and thermodynamic state functions. Application of machine learning techniques can create models to assess protein-ligand potential energy and binding affinity. These methods show superior predictive performance when compared with classical scoring functions available in docking programs. Objective: Our purpose here is to review the development and application of the program SAnDReS. We describe the creation of machine learning models to assess the binding affinity of protein-ligand complexes. Method: SAnDReS implements machine learning methods available in the scikit-learn library. This program is available for download at https://github.com/azevedolab/sandres. SAnDReS uses crystallographic structures, binding, and thermodynamic data to create targeted scoring functions. Results: Recent applications of the program SAnDReS to drug targets such as Coagulation factor Xa, cyclin-dependent kinases, and HIV-1 protease were able to create targeted scoring functions to predict inhibition of these proteins. These targeted models outperform classical scoring functions. Conclusion: Here, we reviewed the development of machine learning scoring functions to predict binding affinity through the application of the program SAnDReS. Our studies show the superior predictive performance of the SAnDReS-developed models when compared with classical scoring functions available in the programs such as AutoDock4, Molegro Virtual Docker, and AutoDock Vina.


2021 ◽  
Vol 11 (13) ◽  
pp. 5956
Author(s):  
Elena Parra ◽  
Irene Alice Chicchi Giglioli ◽  
Jestine Philip ◽  
Lucia Amalia Carrasco-Ribelles ◽  
Javier Marín-Morales ◽  
...  

In this article, we introduce three-dimensional Serious Games (3DSGs) under an evidence-centered design (ECD) framework and use an organizational neuroscience-based eye-tracking measure to capture implicit behavioral signals associated with leadership skills. While ECD is a well-established framework used in the design and development of assessments, it has rarely been utilized in organizational research. The study proposes a novel 3DSG combined with organizational neuroscience methods as a promising tool to assess and recognize leadership-related behavioral patterns that manifest during complex and realistic social situations. We offer a research protocol for assessing task- and relationship-oriented leadership skills that uses ECD, eye-tracking measures, and machine learning. Seamlessly embedding biological measures into 3DSGs enables objective assessment methods that are based on machine learning techniques to achieve high ecological validity. We conclude by describing a future research agenda for the combined use of 3DSGs and organizational neuroscience methods for leadership and human resources.


mSphere ◽  
2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Emily G. Sweeney ◽  
Andrew Nishida ◽  
Alexandra Weston ◽  
Maria S. Bañuelos ◽  
Kristin Potter ◽  
...  

ABSTRACTBacteria are often found living in aggregated multicellular communities known as biofilms. Biofilms are three-dimensional structures that confer distinct physical and biological properties to the collective of cells living within them. We used agent-based modeling to explore whether local cellular interactions were sufficient to give rise to global structural features of biofilms. Specifically, we asked whether chemorepulsion from a self-produced quorum-sensing molecule, autoinducer-2 (AI-2), was sufficient to recapitulate biofilm growth and cellular organization observed for biofilms ofHelicobacter pylori, a common bacterial resident of human stomachs. To carry out this modeling, we modified an existing platform, Individual-based Dynamics of Microbial Communities Simulator (iDynoMiCS), to incorporate three-dimensional chemotaxis, planktonic cells that could join or leave the biofilm structure, and cellular production of AI-2. We simulated biofilm growth of previously characterizedH. pyloristrains with various AI-2 production and sensing capacities. Using biologically plausible parameters, we were able to recapitulate both the variation in biofilm mass and cellular distributions observed with these strains. Specifically, the strains that were competent to chemotax away from AI-2 produced smaller and more heterogeneously spaced biofilms, whereas the AI-2 chemotaxis-defective strains produced larger and more homogeneously spaced biofilms. The model also provided new insights into the cellular demographics contributing to the biofilm patterning of each strain. Our analysis supports the idea that cellular interactions at small spatial and temporal scales are sufficient to give rise to larger-scale emergent properties of biofilms.IMPORTANCEMost bacteria exist in aggregated, three-dimensional structures called biofilms. Although biofilms play important ecological roles in natural and engineered settings, they can also pose societal problems, for example, when they grow in plumbing systems or on medical implants. Understanding the processes that promote the growth and disassembly of biofilms could lead to better strategies to manage these structures. We had previously shown thatHelicobacter pyloribacteria are repulsed by high concentrations of a self-produced molecule, AI-2, and thatH. pylorimutants deficient in AI-2 sensing form larger and more homogeneously spaced biofilms. Here, we used computer simulations of biofilm formation to show that localH. pyloribehavior of repulsion from high AI-2 could explain the overall architecture ofH. pyloribiofilms. Our findings demonstrate that it is possible to change global biofilm organization by manipulating local cell behaviors, which suggests that simple strategies targeting cells at local scales could be useful for controlling biofilms in industrial and medical settings.


2021 ◽  
Vol 7 (21) ◽  
pp. eabg3032
Author(s):  
Jana Petrović ◽  
Alf Göök ◽  
Bo Cederwall

We introduce a neutron-gamma emission tomography (NGET) technique for rapid detection, three-dimensional imaging, and characterization of special nuclear materials like weapons-grade plutonium and uranium. The technique is adapted from fundamental nuclear physics research and represents a previously unexplored approach to the detection and imaging of small quantities of these materials. The method is demonstrated on a radiation portal monitor prototype system based on fast organic scintillators, measuring the characteristic fast time and energy correlations between particles emitted in nuclear fission processes. The use of these correlations in real time in conjunction with modern machine learning techniques provides unprecedented imaging efficiency and high spatial resolution. This imaging modality addresses global security threats from terrorism and the proliferation of nuclear weapons. It also provides enhanced capabilities for addressing different nuclear accident scenarios and for environmental radiological surveying.


2012 ◽  
Vol 21 (9) ◽  
pp. 2021-2032 ◽  
Author(s):  
Silvia Claros ◽  
Noela Rodríguez-Losada ◽  
Encarnación Cruz ◽  
Enrique Guerado ◽  
José Becerra ◽  
...  

2021 ◽  
Vol 9 (2) ◽  
pp. 417
Author(s):  
Sherli Koshy-Chenthittayil ◽  
Linda Archambault ◽  
Dhananjai Senthilkumar ◽  
Reinhard Laubenbacher ◽  
Pedro Mendes ◽  
...  

The human microbiome has been a focus of intense study in recent years. Most of the living organisms comprising the microbiome exist in the form of biofilms on mucosal surfaces lining our digestive, respiratory, and genito-urinary tracts. While health-associated microbiota contribute to digestion, provide essential nutrients, and protect us from pathogens, disturbances due to illness or medical interventions contribute to infections, some that can be fatal. Myriad biological processes influence the make-up of the microbiota, for example: growth, division, death, and production of extracellular polymers (EPS), and metabolites. Inter-species interactions include competition, inhibition, and symbiosis. Computational models are becoming widely used to better understand these interactions. Agent-based modeling is a particularly useful computational approach to implement the various complex interactions in microbial communities when appropriately combined with an experimental approach. In these models, each cell is represented as an autonomous agent with its own set of rules, with different rules for each species. In this review, we will discuss innovations in agent-based modeling of biofilms and the microbiota in the past five years from the biological and mathematical perspectives and discuss how agent-based models can be further utilized to enhance our comprehension of the complex world of polymicrobial biofilms and the microbiome.


Author(s):  
P.G Young ◽  
T.B.H Beresford-West ◽  
S.R.L Coward ◽  
B Notarberardino ◽  
B Walker ◽  
...  

Image-based meshing is opening up exciting new possibilities for the application of computational continuum mechanics methods (finite-element and computational fluid dynamics) to a wide range of biomechanical and biomedical problems that were previously intractable owing to the difficulty in obtaining suitably realistic models. Innovative surface and volume mesh generation techniques have recently been developed, which convert three-dimensional imaging data, as obtained from magnetic resonance imaging, computed tomography, micro-CT and ultrasound, for example, directly into meshes suitable for use in physics-based simulations. These techniques have several key advantages, including the ability to robustly generate meshes for topologies of arbitrary complexity (such as bioscaffolds or composite micro-architectures) and with any number of constituent materials (multi-part modelling), providing meshes in which the geometric accuracy of mesh domains is only dependent on the image accuracy (image-based accuracy) and the ability for certain problems to model material inhomogeneity by assigning the properties based on image signal strength. Commonly used mesh generation techniques will be compared with the proposed enhanced volumetric marching cubes (EVoMaCs) approach and some issues specific to simulations based on three-dimensional image data will be discussed. A number of case studies will be presented to illustrate how these techniques can be used effectively across a wide range of problems from characterization of micro-scaffolds through to head impact modelling.


2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Masaya Hagiwara ◽  
Hisataka Maruyama ◽  
Masakazu Akiyama ◽  
Isabel Koh ◽  
Fumihito Arai

AbstractCollective migration of epithelial cells is a fundamental process in multicellular pattern formation. As they expand their territory, cells are exposed to various physical forces generated by cell–cell interactions and the surrounding microenvironment. While the physical stress applied by neighbouring cells has been well studied, little is known about how the niches that surround cells are spatio-temporally remodelled to regulate collective cell migration and pattern formation. Here, we analysed how the spatio-temporally remodelled extracellular matrix (ECM) alters the resistance force exerted on cells so that the cells can expand their territory. Multiple microfabrication techniques, optical tweezers, as well as mathematical models were employed to prove the simultaneous construction and breakage of ECM during cellular movement, and to show that this modification of the surrounding environment can guide cellular movement. Furthermore, by artificially remodelling the microenvironment, we showed that the directionality of collective cell migration, as well as the three-dimensional branch pattern formation of lung epithelial cells, can be controlled. Our results thus confirm that active remodelling of cellular microenvironment modulates the physical forces exerted on cells by the ECM, which contributes to the directionality of collective cell migration and consequently, pattern formation.


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