scholarly journals Comparing Individual-Based Approaches to Modelling the Self-Organization of Multicellular Tissues

2016 ◽  
Author(s):  
James M. Osborne ◽  
Alexander G. Fletcher ◽  
Joseph M. Pitt-Francis ◽  
Philip K. Maini ◽  
David J. Gavaghan
Keyword(s):  
Case Studies ◽  
Computational Models ◽  
Experimental Studies ◽  
Computational Modelling ◽  
Tissue Growth ◽  
Self Organization ◽  
Final Approval ◽  
Computational Framework ◽  
Cellular Processes ◽  
Modelling Approaches

AbstractThe coordinated behaviour of populations of cells plays a central role in tissue growth and renewal. Cells react to their microenvironment by modulating processes such as movement, growth and proliferation, and signalling. Alongside experimental studies, computational models offer a useful means by which to investigate these processes. To this end a variety of cell-based modelling approaches have been developed, ranging from lattice-based cellular automata to lattice-free models that treat cells as point-like particles or extended shapes. It is difficult to accurately compare between different modelling approaches, since one cannot distinguish between differences in behaviour due to the underlying model assumptions and those due to differences in the numerical implementation of the model. Here, we exploit the availability of an implementation of five popular cell-based modelling approaches within a consistent computational framework, Chaste (http://www.cs.ox.ac.uk/chaste). This framework allows one to easily change constitutive assumptions within these models. In each case we provide full details of all technical aspects of our model implementations. We compare model implementations using four case studies, chosen to reflect the key cellular processes of proliferation, adhesion, and short-and long-range signalling. These case studies demonstrate the applicability of each model and provide a guide for model usage.Authors’ contributionsJO and AF conceived of the study, designed the study, coordinated the study, carried out the computational modelling and drafted the manuscript. JP contributed to the computational modelling and helped draft the manuscript. PM and DG conceived of the study, designed the study and helped draft the manuscript. All authors gave final approval for publication.

scholarly journals Active Inference as a Computational Framework for Consciousness

2021 ◽  
Author(s):  
Martina G. Vilas ◽  
Ryszard Auksztulewicz ◽  
Lucia Melloni
Keyword(s):  
Empirical Data ◽  
Computational Models ◽  
Computational Modelling ◽  
Process Theory ◽  
Future Research ◽  
Active Inference ◽  
Structural Validity ◽  
Computational Framework ◽  
Validity Testing ◽  
Neural Data

AbstractRecently, the mechanistic framework of active inference has been put forward as a principled foundation to develop an overarching theory of consciousness which would help address conceptual disparities in the field (Wiese 2018; Hohwy and Seth 2020). For that promise to bear out, we argue that current proposals resting on the active inference scheme need refinement to become a process theory of consciousness. One way of improving a theory in mechanistic terms is to use formalisms such as computational models that implement, attune and validate the conceptual notions put forward. Here, we examine how computational modelling approaches have been used to refine the theoretical proposals linking active inference and consciousness, with a focus on the extent and success to which they have been developed to accommodate different facets of consciousness and experimental paradigms, as well as how simulations and empirical data have been used to test and improve these computational models. While current attempts using this approach have shown promising results, we argue they remain preliminary in nature. To refine their predictive and structural validity, testing those models against empirical data is needed i.e., new and unobserved neural data. A remaining challenge for active inference to become a theory of consciousness is to generalize the model to accommodate the broad range of consciousness explananda; and in particular to account for the phenomenological aspects of experience. Notwithstanding these gaps, this approach has proven to be a valuable avenue for theory advancement and holds great potential for future research.

scholarly journals Understanding sex differences in long-term blood pressure regulation: insights from experimental studies and computational modeling

2019 ◽  
Vol 316 (5) ◽  
pp. H1113-H1123 ◽  
Author(s):  
Sameed Ahmed ◽  
Rui Hu ◽  
Jessica Leete ◽  
Anita T. Layton
Keyword(s):  
Blood Pressure ◽  
Sex Differences ◽  
Computational Models ◽  
Experimental Studies ◽  
Pressure Control ◽  
Blood Pressure Regulation ◽  
Pressure Regulation ◽  
Physiological Processes ◽  
Key Players

Sex differences in blood pressure and the prevalence of hypertension are found in humans and animal models. Moreover, there has been a recent explosion of data concerning sex differences in nitric oxide, the renin-angiotensin-aldosterone system, inflammation, and kidney function. These data have the potential to reveal the mechanisms underlying male-female differences in blood pressure control. To elucidate the interactions among the multitude of physiological processes involved, one may apply computational models. In this review, we describe published computational models that represent key players in blood pressure regulation, and highlight sex-specific models and their findings.

scholarly journals Cross-Predicting Essential Genes between Two Model Eukaryotic Species Using Machine Learning

2021 ◽  
Vol 22 (10) ◽  
pp. 5056
Author(s):  
Tulio L. Campos ◽  
Pasi K. Korhonen ◽  
Neil D. Young
Keyword(s):  
Machine Learning ◽  
Experimental Studies ◽  
Essential Genes ◽  
Subcellular Localisation ◽  
Cross Prediction ◽  
Gene Essentiality ◽  
C Elegans ◽  
Cellular Processes ◽  
Eukaryotic Species ◽  
The Cross

Experimental studies of Caenorhabditis elegans and Drosophila melanogaster have contributed substantially to our understanding of molecular and cellular processes in metazoans at large. Since the publication of their genomes, functional genomic investigations have identified genes that are essential or non-essential for survival in each species. Recently, a range of features linked to gene essentiality have been inferred using a machine learning (ML)-based approach, allowing essentiality predictions within a species. Nevertheless, predictions between species are still elusive. Here, we undertake a comprehensive study using ML to discover and validate features of essential genes common to both C. elegans and D. melanogaster. We demonstrate that the cross-species prediction of gene essentiality is possible using a subset of features linked to nucleotide/protein sequences, protein orthology and subcellular localisation, single-cell RNA-seq, and histone methylation markers. Complementary analyses showed that essential genes are enriched for transcription and translation functions and are preferentially located away from heterochromatin regions of C. elegans and D. melanogaster chromosomes. The present work should enable the cross-prediction of essential genes between model and non-model metazoans.

Inverse Ostwald Ripening and Self-Organization of Nanoclusters due to Ion Irradiation

2000 ◽  
Vol 647 ◽  
Author(s):  
K.-H. Heinig ◽  
B. Schmidt ◽  
M. Strobel ◽  
H. Bernas
Keyword(s):  
Phase Separation ◽  
Ion Implantation ◽  
Ostwald Ripening ◽  
Ion Irradiation ◽  
Experimental Studies ◽  
Self Organization ◽  
Mathematical Treatment ◽  
Thermodynamical Equilibrium ◽  
Unusual Behavior ◽  
Thermally Activated

AbstractUnder ion irradiation collisional mixing competes with phase separation if the irradiated solid consists of immiscible components. If a component is a chemical compound, there is another competition between the collisional forced chemical dissociation of the compound and its thermally activated re-formation. Especially at interfaces between immiscible components, irradiation processes far from thermodynamical equilibrium may lead to new phenomena. If the formation of nanoclusters (NCs) occurs during ion implantation, the phase separation caused by ion implantation induced supersaturation can be superimposed by phenomena caused by collisional mixing. In this contribution it will be studied how collisional mixing during high-fluence ion implantation affects NC synthesis and how ion irradiation through a layer of NCs modifies their size and size distribution. Inverse Ostwald ripening of NCs will be predicted theoretically and by kinetic lattice Monte-Carlo simulations. The mathematical treatment of the competition between irradiation-induced detachment of atoms from clusters and their thermally activated diffusion leads to a Gibbs-Thomson relation with modified parameters. The predictions have been confirmed by experimental studies of the evolution of Au NCs in SiO2 irradiated by MeV ions. The unusual behavior results from an effective negative capillary length, which will be shown to be the reason for inverse Ostwald ripening. Another new phenomenon to be addressed is self-organization of NCs in a d-layer parallel to the Si/SiO2 interface. Such d-layers were found when the damage level at the interface was of the order of 1-3 dpa. It will be discussed that the origin of the d-layer of NCs can be assigned to two different mechanisms: (i) The negative interface energy due to collisional mixing gives rise to the formation of tiny clusters of substrate material in front of the interface, which promotes heteronucleation of the implanted impurities. (ii) Collisional mixing in the SiO2produces diffusing oxygen, which may be consumed by the Si/SiO2 interface. A thin layer parallel to the interface becomes denuded of diffusing oxygen, which results in a strong pile up of Si excess. This Si excess promotes heteronucleation too. Independent of the dominating mechanism of self-organization of a d-layer of NCs, its location in SiO2 close to the SiO2/Si interface makes it interesting for non-volatile memory application.

scholarly journals Self-organizing dominance hierarchies in a wild primate population

2015 ◽  
Vol 282 (1814) ◽  
pp. 20151512 ◽  
Author(s):  
Mathias Franz ◽  
Emily McLean ◽  
Jenny Tung ◽  
Jeanne Altmann ◽  
Susan C. Alberts
Keyword(s):  
Individual Differences ◽  
Social Dynamics ◽  
Experimental Studies ◽  
Self Organization ◽  
Social Self ◽  
Dominance Hierarchies ◽  
Change Over Time ◽  
Winner And Loser Effects ◽  
The Social ◽  
Over Time

Linear dominance hierarchies, which are common in social animals, can profoundly influence access to limited resources, reproductive opportunities and health. In spite of their importance, the mechanisms that govern the dynamics of such hierarchies remain unclear. Two hypotheses explain how linear hierarchies might emerge and change over time. The ‘prior attributes hypothesis’ posits that individual differences in fighting ability directly determine dominance ranks. By contrast, the ‘social dynamics hypothesis’ posits that dominance ranks emerge from social self-organization dynamics such as winner and loser effects. While the prior attributes hypothesis is well supported in the literature, current support for the social dynamics hypothesis is limited to experimental studies that artificially eliminate or minimize individual differences in fighting abilities. Here, we present the first evidence supporting the social dynamics hypothesis in a wild population. Specifically, we test for winner and loser effects on male hierarchy dynamics in wild baboons, using a novel statistical approach based on the Elo rating method for cardinal rank assignment, which enables the detection of winner and loser effects in uncontrolled group settings. Our results demonstrate (i) the presence of winner and loser effects, and (ii) that individual susceptibility to such effects may have a genetic basis. Taken together, our results show that both social self-organization dynamics and prior attributes can combine to influence hierarchy dynamics even when agonistic interactions are strongly influenced by differences in individual attributes. We hypothesize that, despite variation in individual attributes, winner and loser effects exist (i) because these effects could be particularly beneficial when fighting abilities in other group members change over time, and (ii) because the coevolution of prior attributes and winner and loser effects maintains a balance of both effects.

scholarly journals Error Propagation in the Simulation of Atherosclerotic Plaque Growth and the Prediction of Atherosclerotic Disease Progression

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2306
Author(s):  
Antonis I. Sakellarios ◽  
Panagiotis Siogkas ◽  
Vassiliki Kigka ◽  
Panagiota Tsompou ◽  
Dimitrios Pleouras ◽  
...  
Keyword(s):  
Error Propagation ◽  
Computational Models ◽  
Computational Modelling ◽  
Predictive Modelling ◽  
Prognostic Models ◽  
Current Analysis ◽  
Non Invasive ◽  
Plaque Growth ◽  
Artery Disease ◽  
Diagnostic Index

Assessments of coronary artery disease can be achieved using non-invasive computed tomography coronary angiography (CTCA). CTCA can be further used for the 3D reconstruction of the coronary arteries and the development of computational models. However, image acquisition and arterial reconstruction introduce an error which can be propagated, affecting the computational results and the accuracy of diagnostic and prognostic models. In this work, we investigate the effect of an imaging error, propagated to a diagnostic index calculated using computational modelling of blood flow and then to prognostic models based on plaque growth modelling or binary logistic predictive modelling. The analysis was performed utilizing data from 20 patients collected at two time points with interscan period of six years. The collected data includes clinical and risk factors, biological and biohumoral data, and CTCA imaging. The results demonstrated that the error propagated and may have significantly affected some of the final outcomes. The calculated propagated error seemed to be minor for shear stress, but was major for some variables of the plaque growth model. In parallel, in the current analysis SmartFFR was not considerably affected, with the limitation of only one case located into the gray zone.

scholarly journals Neuronal and Astrocytic Regulations in Schizophrenia: A Computational Modelling Study

2021 ◽  
Vol 15 ◽  
Author(s):  
Lea Fritschi ◽  
Johanna Hedlund Lindmar ◽  
Florian Scheidl ◽  
Kerstin Lenk
Keyword(s):  
Computational Models ◽  
Glutamate Release ◽  
Signal Transmission ◽  
Computational Modelling ◽  
Atp Release ◽  
Network Activity ◽  
Inhibitory Neurons ◽  
Neuronal Network Activity ◽  
Cell Densities ◽  
Synapse Model

According to the tripartite synapse model, astrocytes have a modulatory effect on neuronal signal transmission. More recently, astrocyte malfunction has been associated with psychiatric diseases such as schizophrenia. Several hypotheses have been proposed on the pathological mechanisms of astrocytes in schizophrenia. For example, post-mortem examinations have revealed a reduced astrocytic density in patients with schizophrenia. Another hypothesis suggests that disease symptoms are linked to an abnormality of glutamate transmission, which is also regulated by astrocytes (glutamate hypothesis of schizophrenia). Electrophysiological findings indicate a dispute over whether the disorder causes an increase or a decrease in neuronal and astrocytic activity. Moreover, there is no consensus as to which molecular pathways and network mechanisms are altered in schizophrenia. Computational models can aid the process in finding the underlying pathological malfunctions. The effect of astrocytes on the activity of neuron-astrocyte networks has been analysed with computational models. These can reproduce experimentally observed phenomena, such as astrocytic modulation of spike and burst signalling in neuron-astrocyte networks. Using an established computational neuron-astrocyte network model, we simulate experimental data of healthy and pathological networks by using different neuronal and astrocytic parameter configurations. In our simulations, the reduction of neuronal or astrocytic cell densities yields decreased glutamate levels and a statistically significant reduction in the network activity. Amplifications of the astrocytic ATP release toward postsynaptic terminals also reduced the network activity and resulted in temporarily increased glutamate levels. In contrast, reducing either the glutamate release or re-uptake in astrocytes resulted in higher network activities. Similarly, an increase in synaptic weights of excitatory or inhibitory neurons raises the excitability of individual cells and elevates the activation level of the network. To conclude, our simulations suggest that the impairment of both neurons and astrocytes disturbs the neuronal network activity in schizophrenia.

scholarly journals Weaving the Common Threads of Simulation and Formation studies in Archaeology

2017 ◽  
Author(s):  
Benjamin Davies
Keyword(s):  
Computer Simulation ◽  
Human Activity ◽  
Computational Models ◽  
Experimental Studies ◽  
Surface Erosion ◽  
Archaeological Record ◽  
The Common ◽  
The Relationship ◽  
Material Record

Computer simulation is a tool increasingly used by archaeologists to build theories about past human activity; however, simulation has had a limited role theorising about the relationship between past behaviours and the formation of observed patterning in the material record. This paper visits the argument for using simulation as a means of addressing the gap that exists between archaeological interpretations of past behaviours and their physical residues. It is argued that simulation is used for much the same reason that archaeologists use ethnographic or experimental studies, and that computational models can help to address some of the practical limitations of these approaches to record formation. A case study from arid Australia, examining the effects of episodic surface erosion on the visibility of the record, shows how simple, generative simulations, grounded in formational logic, can be used to compare different explanatory mechanisms and suggest tests of the archaeological record itself.

scholarly journals What is the role of computational models in Cognitive Science? A quantitative and qualitative analysis of the history of the TRACE model of speech segmentation

2019 ◽  
Author(s):  
Manisha Chawla ◽  
Richard Shillcock
Keyword(s):  
Qualitative Analysis ◽  
Cognitive Science ◽  
Computational Models ◽  
Computational Modelling ◽  
Speech Segmentation ◽  
Cognitive Modelling ◽  
Trace Model ◽  
History Of ◽  
Central Paradigm

Implemented computational models are a central paradigm of Cognitive Science. How do cognitive scientists really use such models? We take the example of one of the most successful and influential cognitive models, TRACE (McClelland & Elman, 1986), and we map its impact on the field in terms of published, electronically available documents that cite the original TRACE paper over a period of 25 years since its publication. We draw conclusions about the general status of computational cognitive modelling and make critical suggestions regarding the nature of abstraction in computational modelling.

Magnetodynamics and Self-Organization in Strongly Non-Equilibrium Ferrosuspensions

2009 ◽  
Vol 152-153 ◽  
pp. 175-181
Author(s):  
Bronislav Kashevsky ◽  
Sergei Kashevsky ◽  
Igor Prokhorov
Keyword(s):  
Smart Materials ◽  
Materials Science ◽  
Magnetic Hysteresis ◽  
Experimental Studies ◽  
Rotating Magnetic Field ◽  
Self Organization ◽  
Linearly Polarized ◽  
Dilute Suspension ◽  
Soft Particles ◽  
Non Equilibrium

This paper presents computational and experimental studies of two phenomena occurring in magnetic suspensions under strongly non-equilibrium conditions created by high-frequency (in comparison with the inverse characteristic time of the particle mechanical motion) magnetic fields. First is the dynamic magnetic hysteresis in a dilute suspension of highly-coercive particles subjected to linearly polarized fields. Energy absorption by particles is of great interest for cancer treatment, chemical technology, biology and smart materials science. Second is related to polymer composite technologies and represents dissipative self-organization of a system of magnetically soft particles in a drying thin layer of polymer solution set under a rotating magnetic field

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