computational modeling of microbial communities using GEnome-scale Models (GEMs) of metabolism is a new frontier in systems biology. Here, we discuss recent developments in this area ranging from high-throughput GEMs ...
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computational modeling of microbial communities using GEnome-scale Models (GEMs) of metabolism is a new frontier in systems biology. Here, we discuss recent developments in this area ranging from high-throughput GEMs reconstruction pipelines to approaches for modeling under steady-state and for simulating temporal, evolutionary, and spatiotemporal dynamics of microbial communities. We categorize these approaches based on flux balance analysis or elementary mode analysis of mixed-bag and compartmentalized GEMs and discuss their scope of applications and scalability for large-scale simulations. In addition, we review computational tools using GEMs for the design of microbial communities and recent efforts to integrate GEMs and machine learning for predicting interspecies interactions. We conclude by discussing best practices for using these tools and potential avenues for future development.
Despite the number of clinical and experimental surveys, a computational model of cancer growth in which the abundance of data can be structured and understood is lacking. The goal of this project was to provide a com...
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ISBN:
(纸本)9781665412469
Despite the number of clinical and experimental surveys, a computational model of cancer growth in which the abundance of data can be structured and understood is lacking. The goal of this project was to provide a comprehensive and expandable simulation method to predict and visualize cancer and microvascular network growth. This novel method offers the advantage of a multiscale model that incorporates data from in vivo microscale computed tomography (microCT) images of the microvasculature in breast cancer-bearing animals. We use a lattice-based model that is designed so that different evolutionary scenarios can be established to predict the impact of nutrient stress on tumor morphology and growth patterns. Overall, simulation results show tumor progression similar to that known to occur in clinical practice.
Social decision-making is guided by a complex set of social norms. computational modeling can play a significant role in enriching our understanding of these norms and how precisely they direct social choices. Here, w...
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Social decision-making is guided by a complex set of social norms. computational modeling can play a significant role in enriching our understanding of these norms and how precisely they direct social choices. Here, we highlight three major advantages to using computational modeling, particularly models derived from Utility Theory, in the study of social norms. We illustrate how such models can help generate detailed processes of decision-making, enforce theoretical precision by delineating abstract concepts, and unpack when, and why, people adhere to specific social norms. For each benefit, we discuss a recent study which has employed modeling in the service of assessing the role of norms in decision-making, collectively revealing how computational modeling enables better prediction, description, and explanation of important social choices.
In the retina, signals originating from rod and cone photoreceptors can reach retinal ganglion cells (RGCs) through different pathways. The secondary rod pathway is functionally important under mesopic light, such as ...
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In the retina, signals originating from rod and cone photoreceptors can reach retinal ganglion cells (RGCs) through different pathways. The secondary rod pathway is functionally important under mesopic light, such as at dawn or dusk. Electrical synapses (gap junctions) between rods and cones form the entry to the secondary rod pathway. Recent experimental observations indicate that these gap junctions are plastic, changing with the time of day and/or light/dark adaptation. However, the impact of this plasticity on the contribution of the secondary rod pathway to RGC activity remains elusive. Here, we model the spatial topology and connectivity of the secondary rod pathway of the mouse retina and test the light responses of OFF RGCs to dim light stimuli. Our results corroborate previously published electrical recordings thereby establishing the validity of the model and its suitability to further study rod/cone coupling plasticity.
computational models of infant word-finding typically operate over transcriptions of infant-directed speech corpora. It is now possible to test models of word segmentation on speech materials, rather than transcriptio...
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computational models of infant word-finding typically operate over transcriptions of infant-directed speech corpora. It is now possible to test models of word segmentation on speech materials, rather than transcriptions of speech. We propose that such modeling efforts be conducted over the speech of the experimental stimuli used in studies measuring infants' capacity for learning from spoken sentences. Correspondence with infant outcomes in such experiments is an appropriate benchmark for models of infants. We demonstrate such an analysis by applying the DP-Parser model of Algayres and colleagues to auditory stimuli used in infant psycholinguistic experiments by Pelucchi and colleagues. The DP-Parser model takes speech as input, and creates multiple overlapping embeddings from each utterance. Prospective words are identified as clusters of similar embedded segments. This allows segmentation of each utterance into possible words, using a dynamic programming method that maximizes the frequency of constituent segments. We show that DP-Parse mimics American English learners' performance in extracting words from Italian sentences, favoring the segmentation of words with high syllabic transitional probability. This kind of computational analysis over actual stimuli from infant experiments may be helpful in tuning future models to match human performance.
Major depression disorder (MDD) is a prevalent but severe psychiatric disorder, yet it is unclear how MDD is induced. Recently, accumulated evidence of in vivo animal experiments suggested that the lateral habenula (L...
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Major depression disorder (MDD) is a prevalent but severe psychiatric disorder, yet it is unclear how MDD is induced. Recently, accumulated evidence of in vivo animal experiments suggested that the lateral habenula (LHb) might play an important role in regulating the brain's dopamine (DA)ergic mechanism. Some abnormal changes and activities in the LHb region may lead to the excessive inhibition of the downstream DAergic neurons and thus induce MDD symptoms. However, how these abnormalities in the LHb cause the excessive inhibition of the DAergic neurons is still not clearly demonstrated. In this paper, we built a computational model for the pathway between the LHb and the ventral tegmental area (VTA) to simulate the different neurophysiological processes with the LHb regulation in healthy and MDD individuals. From the simulation results of the LHb-VTA pathway model we found that both the firing pattern conversion of the LHb neuron from regular tonic to burst, and the overexpression of Kir channel proteins on the LHb astrocyte membrane could more strongly inhibit the VTA DAergic neuron, which corresponds to the experimental results of previous in vivo animal studies. Our model and simulation results may shed light on future studies to further understand the pathology of MDD.
Spinal cord stimulation (SCS) is a neuromodulation technique that applies electrical stimulation to the spinal cord to alter neural activity or processing. While SCS has historically been used as a last-resort therapy...
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Spinal cord stimulation (SCS) is a neuromodulation technique that applies electrical stimulation to the spinal cord to alter neural activity or processing. While SCS has historically been used as a last-resort therapy for chronic pain management, novel applications and technologies have recently been developed that either increase the efficacy of treatment for chronic pain or drive neural activity to produce muscular activity/movement following a paralyzing spinal cord injury (SCI). Despite these recent innovations, there remain fundamental questions concerning the neural recruitment underlying these efficacious results. This work evaluated the neural activity and mechanisms for three SCS applications: both conventional SCS and closed-loop SCS for pain management, as well as ventral, high frequency spinal cord stimulation (HF-SCS) for inspiratory muscle activation following a SCI. I developed computational models to both predict the neural response to SCS and explore factors influencing neural activation. Models consisted of three components: a finite element model (FEM) of the spinal cord to predict the potential fields generated by stimulation, biophysical neuron models, and algorithms to apply time-dependent extracellular potentials to the neuron models and simulate their response. While this cutting-edge modeling methodology could be used to predict neural activity following stimulation, it was unclear how anatomical and technical factors affected neural predictions. To evaluate these factors, I designed an FEM of a T9 thoracic spine with an implanted electrode array. Then, I sequentially removed details from the model and quantified the changes in neural predictions. I identified several factors with large (>30%) effects on neural thresholds, including overall electrode impedance (for voltage-controlled stimulation), the electrode position relative to the spine, and dura mater conductivity. This work will be invaluable for basic science and clinical application
Despite the declaration of SARS-CoV-2 infection as a pandemic on March 11, 2020 by the World Health Organization, till date, no effective treatment has been approved to combat the severity of this pandemic. Few antivi...
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Despite the declaration of SARS-CoV-2 infection as a pandemic on March 11, 2020 by the World Health Organization, till date, no effective treatment has been approved to combat the severity of this pandemic. Few antiviral and antimalarial drugs have been used for symptomatic treatment of COVID-19 infection, but none of the treatment strategies has provided promising results against this infection. As the journey of a vaccine is also very long, there is a need to repurpose the already approved drugs against this pandemic. The urge of studying the structural association of human ACE2, an entry receptor of the virus in the human body with the SARS-CoV-2, may lead to the invention of a new promising prophylactic as well as curative treatment options for the COVID-19 pandemic. This chapter covers the importance of human ACE2 receptors in pathophysiology of COVID-19 infection. Finally, the results of various computational studies have been elaborated in this chapter. The role of different classes of already approved drugs which have shown promise against SARS-CoV-2 infection in in silico docking and computational modeling experiments is summarized for their future prospects in the treatment of COVID-19 infection. They were chosen on the basis of their free binding energies and relative ligand scoring scales. Molecular docking tools like molecule operating environment (MOE), Glide, AutoDock Vina, and Swiss dock were used in many in silico experiments. Some of the potential treatment modalities found in literature survey were ACE inhibitors, antibiotics, anti-inflammatory, antineoplastic, phosphodiesterase inhibitors, hydroxychloroquine and chloroquine, and so on, which have been proved to be efficacious treatment options for SARS-CoV-2 infection by computational modeling of human ACE2 receptors. less
Future human aerospace systems will consist of a complex integration of multiple technologies, including Artificial Intelligence (AI) driven autonomy. We expect autonomous systems to support and augment human performa...
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Future human aerospace systems will consist of a complex integration of multiple technologies, including Artificial Intelligence (AI) driven autonomy. We expect autonomous systems to support and augment human performance, especially when humans are experiencing physiological or cognitive decrements in operational scenarios. To accomplish this, we need the ability to identify these human performance decrements in different individuals and in different mission contexts (e.g., varying g-levels, acceleration profiles). Factors such as sex, weight, and height can alter physiological response to various stressors. Therefore, accounting for these differences is essential to build effective autonomous systems in these operational contexts. computational models and algorithms that drive our human assessment systems are rooted in theory but also need realistic data for testing and evaluation. While there is a basic understanding of expected changes in physiology under varying stressors, available data are largely lab-based and sparse. However, experimentally determining the response of each individual in a large variety of mission contexts is prohibitively expensive and time-consuming. Thus, future human assessment algorithm development efforts would substantially benefit from simulated, representative datasets created through configurable human models. We will build on prior cardiovascular, metabolic, and other modeling work to create a physiological model tailorable to specific operational mission contexts and personalized input parameters. In particular, we have implemented a 21-compartment lumped-parameter model to simulate physiological responses of a 50 th percentile female and a 50 th percentile male during a parabolic flight maneuver. The modeled individuals were differentiated by anthropometric and total blood volume data based on U.S. Army personnel. Results of the simulations highlight and quantify the differences in physiological responses between the two indivi
HIV-1 integrase (IN) is a key enzyme that is essential for mediating the insertion of retroviral DNA into the host chromosome. IN also exhibits additional functions which are not fully elucidated, including its abilit...
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HIV-1 integrase (IN) is a key enzyme that is essential for mediating the insertion of retroviral DNA into the host chromosome. IN also exhibits additional functions which are not fully elucidated, including its ability to bind to viral genomic RNA. Lack of binding of IN to RNA within the virions has been shown to be associated with production of morphologically defective virus particles. However, the exact structure of HIV-1 IN bound to RNA is not known. Based on the studies that C-terminal domain (CTD) of IN binds to TAR RNA region and based on the observation that TAR and the host factor INI1 binding to IN-CTD are identical, we computationally modelled the IN-CTD/TAR complex structure. computational modeling of nucleic acid binding to proteins is a valuable method to understand the macromolecular interaction when experimental methods of solving the complex structures are not feasible. The current model of the IN-CTD/TAR complex may facilitate further understanding of this interaction and may lead to therapeutic targeting of IN-CTD/RNA interactions to inhibit HIV-1 replication. less
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