Dynamic Computational Models and Simulations of the Opioid Crisis: A Comprehensive Survey

Opioids have been shown to temporarily reduce the severity of pain when prescribed for medical purposes. However, opioid analgesics can also lead to severe adverse physical and psychological effects or even death through misuse, abuse, short- or long-term addiction, and one-time or recurrent overdose. Dynamic computational models and simulations can offer great potential to interpret the complex interaction of the drivers of the opioid crisis and assess intervention strategies. This study surveys existing studies of dynamic computational models and simulations addressing the opioid crisis and provides an overview of the state-of-the-art of dynamic computational models and simulations of the opioid crisis. This review gives a detailed analysis of existing modeling techniques, model conceptualization and formulation, and the policy interventions they suggest. It also explores the data sources they used and the study population they represented. Based on this analysis, direction and opportunities for future dynamic computational models for addressing the opioid crisis are suggested.

Beyond collective intelligence: Collective adaptation

We develop a conceptual framework for studying collective adaptation: the process of iterative co-adaptation of cognitive strategies, social environments, and problem structures. Going beyond searching for “intelligent” collectives, we integrate research from different disciplines to show how collective adaptation perspective can help explain why similar collectives can follow very different and sometimes counter-intuitive trajectories. We further discuss how this perspective explains why successful collectives appear to have a general collective intelligence factor, why collectives rarely optimize their behaviour for a single problem, why their behaviours can appear myopic, and why playful exploration of alternative social systems can be useful. We describe different approaches for the study of collective adaptation, including computational models inspired by evolution and statistical physics. The framework of collective adaptation enables the integration and formalization of knowledge about human collective phenomena and opens doors to a rigorous transdisciplinary pursuit of important outstanding questions about human sociality.

Cognitive agency in sociocultural evolution

Models of sociocultural evolution generally study the population dynamics of cultural traits given known biases in social learning. Cognitive agency, understood as the dynamics underlying a specific agent’s adoption of a given trait, is essentially irrelevant in this framework. This article argues that although implementing and instrumenting agency in computational models is fundamentally challenging, it is ultimately possible and would help us overcome major limitations in our understanding of sociocultural dynamics.Indeed, the behaviour of humans is not causally generated by a set of predefined behavioural laws, but by the situated activity of their cognitive architecture. Idealised models of biased transmission certainly help us understand specific features of population dynamics. However, they distract us from the deep intrication of the cognitive and ecological processes underlying sociocultural evolution, and erase their embodied, subjective nature.In line with the earlier “Thinking Through Other Minds” account of sociocultural evolution, this article highlights how the Active Inference framework can help us implement and instrument computational models that address these limitations. Such models would not only help ground our understanding of sociocultural evolution in the underlying cognitive dynamics, but also help solve (or frame) open questions in the study of ritual, relation between cultural transmission and innovation, as well as scales of cultural evolution.

Unveiling DNA algorithms: satellite DNA sequences as iterable objects. A computational model.

Every adult male of the little roundworm Caenorhabditis elegans is always and invariably comprised of exactly 1031 somatic cells, not one more, not one less; and so it is for the adult hermaphrodite (959 somatic cells); its intestine founder cell (the ‘E’ blastomere), if isolated and cultured, undergoes the same number of divisions as in the whole embryo (Robertson et al., 2014); the zygote of Drosophila melanogaster executes 13 cycles of asynchronous cell divisions without cellularization: how are these numbers counted? Artificial Intelligence (First and Second Order Logic, Knowledge graph Engineering) infers that, to perform precise stereotypical numbers of asynchronous cell divisions, a nucleic (genomic) counter is indispensable. Made up of tandemly repeated similar monomers, satellite DNA (satDNA) corresponds to iterable objects used in programming. The purpose of this article is to show how satDNA sequences can be iterated over to count a deterministic number of cell divisions: computational models (attached for free download) are introduced that handle DNA repeated sequences as iterable counters and simulate their use in cells through an epigenetic marker (cytosine methylation) as an iterator. SatDNA, because of its propensity to remodel its structure, can also operate as a strong accelerator in the evolution of complex organs and provides a basis to control interspecific variability of shapes.

An Accelerated Thrombosis Model for Computational Fluid Dynamics Simulations in Rotary Blood Pumps

Purpose Thrombosis ranks among the major complications in blood-carrying medical devices and a better understanding to influence the design related contribution to thrombosis is desirable. Over the past years many computational models of thrombosis have been developed. However, numerically cheap models able to predict localized thrombus risk in complex geometries are still lacking. The aim of the study was to develop and test a computationally efficient model for thrombus risk prediction in rotary blood pumps. Methods We used a two-stage approach to calculate thrombus risk. The first stage involves the computation of velocity and pressure fields by computational fluid dynamic simulations. At the second stage, platelet activation by mechanical and chemical stimuli was determined through species transport with an Eulerian approach. The model was compared with existing clinical data on thrombus deposition within the HeartMate II. Furthermore, an operating point and model parameter sensitivity analysis was performed. Results Our model shows good correlation (R2 > 0.93) with clinical data and identifies the bearing and outlet stator region of the HeartMate II as the location most prone to thrombus formation. The calculation of thrombus risk requires an additional 10–20 core hours of computation time. Conclusion The concentration of activated platelets can be used as a surrogate and computationally low-cost marker to determine potential risk regions of thrombus deposition in a blood pump. Relative comparisons of thrombus risk are possible even considering the intrinsic uncertainty in model parameters and operating conditions.

Subcellular Remodeling in Filamin C Deficient Mouse Hearts Impairs Myocyte Tension Development during Progression of Dilated Cardiomyopathy

Dilated cardiomyopathy (DCM) is a life-threatening form of heart disease that is typically characterized by progressive thinning of the ventricular walls, chamber dilation, and systolic dysfunction. Multiple mutations in the gene encoding filamin C (FLNC), an actin-binding cytoskeletal protein in cardiomyocytes, have been found in patients with DCM. However, the mechanisms that lead to contractile impairment and DCM in patients with FLNC variants are poorly understood. To determine how FLNC regulates systolic force transmission and DCM remodeling, we used an inducible, cardiac-specific FLNC-knockout (icKO) model to produce a rapid onset of DCM in adult mice. Loss of FLNC reduced systolic force development in single cardiomyocytes and isolated papillary muscles but did not affect twitch kinetics or calcium transients. Electron and immunofluorescence microscopy showed significant defects in Z-disk alignment in icKO mice and altered myofilament lattice geometry. Moreover, a loss of FLNC induces a softening myocyte cortex and structural adaptations at the subcellular level that contribute to disrupted longitudinal force production during contraction. Spatially explicit computational models showed that these structural defects could be explained by a loss of inter-myofibril elastic coupling at the Z-disk. Our work identifies FLNC as a key regulator of the multiscale ultrastructure of cardiomyocytes and therefore plays an important role in maintaining systolic mechanotransmission pathways, the dysfunction of which may be key in driving progressive DCM.

Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process

Both experimental and computational methods for the exploration of structure, functionality, and properties of materials often necessitate the search across broad parameter spaces to discover optimal experimental conditions and regions of interest in the image space or parameter space of computational models. The direct grid search of the parameter space tends to be extremely time-consuming, leading to the development of strategies balancing exploration of unknown parameter spaces and exploitation towards required performance metrics. However, classical Bayesian optimization strategies based on the Gaussian process (GP) do not readily allow for the incorporation of the known physical behaviors or past knowledge. Here we explore a hybrid optimization/exploration algorithm created by augmenting the standard GP with a structured probabilistic model of the expected system’s behavior. This approach balances the flexibility of the non-parametric GP approach with a rigid structure of physical knowledge encoded into the parametric model. The fully Bayesian treatment of the latter allows additional control over the optimization via the selection of priors for the model parameters. The method is demonstrated for a noisy version of the classical objective function used to evaluate optimization algorithms and further extended to physical lattice models. This methodology is expected to be universally suitable for injecting prior knowledge in the form of physical models and past data in the Bayesian optimization framework.

Biomechanical Analyses of Porous Designs of 3D-Printed Titanium Implant for Mandibular Segmental Osteotomy Defects

Clinically, a reconstruction plate can be used for the facial repair of patients with mandibular segmental defects, but it cannot restore their chewing function. The main purpose of this research is to design a new three-dimensionally (3D) printed porous titanium mandibular implant with both facial restoration and oral chewing function reconstruction. Its biomechanical properties were examined using both finite element analysis (FEA) and in vitro experiments. Cone beam computed tomography images of the mandible of a patient with oral cancer were selected as a reference to create 3D computational models of the bone and of the 3D-printed porous implant. The pores of the porous implant were circles or hexagons of 1 or 2 mm in size. A nonporous implant was fabricated as a control model. For the FEA, two chewing modes, namely right unilateral molar clench and right group function, were set as loading conditions. Regarding the boundary condition, the displacement of both condyles was fixed in all directions. For the in vitro experiments, an occlusal force (100 N) was applied to the abutment of the 3D-printed mandibular implants with and without porous designs as the loading condition. The porous mandibular implants withstood higher stress and strain than the nonporous mandibular implant, but all stress values were lower than the yield strength of Ti-6Al-4V (800 MPa). The strain value of the bone surrounding the mandibular implant was affected not only by the shape and size of the pores but also by the chewing mode. According to Frost’s mechanostat theory of bone, higher bone strain under the porous implants might help maintain or improve bone quality and bone strength. The findings of this study serve as a biomechanical reference for the design of 3D-printed titanium mandibular implants and require confirmation through clinical investigations.

Recent Advances in Computational Modeling of Biomechanics and Biorheology of Red Blood Cells in Diabetes

Diabetes mellitus, a metabolic disease characterized by chronically elevated blood glucose levels, affects about 29 million Americans and more than 422 million adults all over the world. Particularly, type 2 diabetes mellitus (T2DM) accounts for 90–95% of the cases of vascular disease and its prevalence is increasing due to the rising obesity rates in modern societies. Although multiple factors associated with diabetes, such as reduced red blood cell (RBC) deformability, enhanced RBC aggregation and adhesion to the endothelium, as well as elevated blood viscosity are thought to contribute to the hemodynamic impairment and vascular occlusion, clinical or experimental studies cannot directly quantify the contributions of these factors to the abnormal hematology in T2DM. Recently, computational modeling has been employed to dissect the impacts of the aberrant biomechanics of diabetic RBCs and their adverse effects on microcirculation. In this review, we summarize the recent advances in the developments and applications of computational models in investigating the abnormal properties of diabetic blood from the cellular level to the vascular level. We expect that this review will motivate and steer the development of new models in this area and shift the attention of the community from conventional laboratory studies to combined experimental and computational investigations, aiming to provide new inspirations for the development of advanced tools to improve our understanding of the pathogenesis and pathology of T2DM.

A Liquid State Perspective on Dynamics of Chromatin Compartments

The interior of the eukaryotic cell nucleus has a crowded and heterogeneous environment packed with chromatin polymers, regulatory proteins, and RNA molecules. Chromatin polymer, assisted by epigenetic modifications, protein and RNA binders, forms multi-scale compartments which help regulate genes in response to cellular signals. Furthermore, chromatin compartments are dynamic and tend to evolve in size and composition in ways that are not fully understood. The latest super-resolution imaging experiments have revealed a much more dynamic and stochastic nature of chromatin compartments than was appreciated before. An emerging mechanism explaining chromatin compartmentalization dynamics is the phase separation of protein and nucleic acids into membraneless liquid condensates. Consequently, concepts and ideas from soft matter and polymer systems have been rapidly entering the lexicon of cell biology. In this respect, the role of computational models is crucial for establishing a rigorous and quantitative foundation for the new concepts and disentangling the complex interplay of forces that contribute to the emergent patterns of chromatin dynamics and organization. Several multi-scale models have emerged to address various aspects of chromatin dynamics, ranging from equilibrium polymer simulations, hybrid non-equilibrium simulations coupling protein binding and chromatin folding, and mesoscopic field-theoretic models. Here, we review these emerging theoretical paradigms and computational models with a particular focus on chromatin’s phase separation and liquid-like properties as a basis for nuclear organization and dynamics.