scholarly journals Using the Mutation-Selection Framework to Characterize Selection on Protein Sequences

Genes ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 409 ◽  
Author(s):  
Ashley Teufel ◽  
Andrew Ritchie ◽  
Claus Wilke ◽  
David Liberles
Keyword(s):  
Protein Sequences ◽  
Original Model ◽  
Mechanistic Modeling ◽  
Mathematical Framework ◽  
Conditional Probabilities ◽  
Modeling Framework ◽  
Mutational Pressure ◽  
Generative Process ◽  
Different Types ◽  
Selection Framework

When mutational pressure is weak, the generative process of protein evolution involves explicit probabilities of mutations of different types coupled to their conditional probabilities of fixation dependent on selection. Establishing this mechanistic modeling framework for the detection of selection has been a goal in the field of molecular evolution. Building on a mathematical framework proposed more than a decade ago, numerous methods have been introduced in an attempt to detect and measure selection on protein sequences. In this review, we discuss the structure of the original model, subsequent advances, and the series of assumptions that these models operate under.

scholarly journals Interpretable Pairwise Distillations for Generative Protein Sequence Models

2021 ◽  
Author(s):  
Christoph Feinauer ◽  
Barthelemy Meynard-Piganeau ◽  
Carlo Lucibello
Keyword(s):  
Neural Network ◽  
Protein Design ◽  
Protein Sequence ◽  
Protein Sequences ◽  
Generative Models ◽  
Modeling Framework ◽  
The Past ◽  
Different Types ◽  
Pairwise Models ◽  
Mutational Effect

Many different types of generative models for protein sequences have been proposed in literature. Their uses include the prediction of mutational effects, protein design and the prediction of structural properties. Neural network (NN) architectures have shown great performances, commonly attributed to the capacity to extract non-trivial higher-order interactions from the data. In this work, we analyze three different NN models and assess how close they are to simple pairwise distributions, which have been used in the past for similar problems. We present an approach for extracting pairwise models from more complex ones using an energy-based modeling framework. We show that for the tested models the extracted pairwise models can replicate the energies of the original models and are also close in performance in tasks like mutational effect prediction.

scholarly journals A Mechanistic Modeling Framework Reveals the Key Principles Underlying Tumor Metabolism

2021 ◽  
Author(s):  
Shubham Tripathi ◽  
Jun Hyoung Park ◽  
Shivanand Pudakalakatti ◽  
Pratip K. Bhattacharya ◽  
Benny Abraham Kaipparettu ◽  
...  
Keyword(s):  
Tumor Cells ◽  
Aerobic Glycolysis ◽  
Mechanistic Model ◽  
Braf Inhibitor ◽  
Tumor Metabolism ◽  
Mechanistic Modeling ◽  
Metabolic Phenotype ◽  
Mathematical Framework ◽  
Modeling Framework ◽  
Metabolic Heterogeneity

AbstractWhile aerobic glycolysis, or the Warburg effect, has for a long time been considered a hallmark of tumor metabolism, recent studies have revealed a far more complex picture. Tumor cells exhibit widespread metabolic heterogeneity, utilizing glycolysis, oxidative phosphorylation, or both, and can switch between different metabolic phenotypes. A framework to analyze the observed metabolic heterogeneity and plasticity is, however, lacking. Using a mechanistic model that includes the key metabolic pathways active in tumor cells, we show that the inhibition of phosphofructokinase by excess ATP in the cytoplasm can drive a preference for aerobic glycolysis in fast-proliferating tumor cells. The differing rates of ATP utilization by tumor cells can therefore drive metabolic heterogeneity. Building upon this idea, we couple the metabolic phenotype of tumor cells to their migratory phenotype, and show that our model predictions are in agreement with previous experiments. We report that the reliance of proliferating cells on different anaplerotic pathways depends on the relative availability of glucose and glutamine, and can further drive metabolic heterogeneity. Finally, using treatment of melanoma cells with a BRAF inhibitor as an example, we show that our model can be used to predict the metabolic and gene expression changes in cancer cells in response to drug treatment. By making predictions that are far more generalizable and interpretable as compared to previous tumor metabolism modeling approaches, our framework identifies key principles that govern tumor cell metabolism, and the reported heterogeneity and plasticity. These principles could be key to targeting the metabolic vulnerabilities of cancer.SignificanceThis study presents an interpretable mathematical framework for analyzing the metabolic heterogeneity and plasticity exhibited by tumor cells.

Satellite Channels Based on IEEE 802.16 Standard

2019 ◽  
pp. 133-166
Keyword(s):  
Frequency Shift ◽  
Communication Channels ◽  
Ieee 802.16 ◽  
Original Model ◽  
Doppler Shifts ◽  
Time Diversity ◽  
Different Types ◽  
Satellite Channels ◽  
The Impact ◽  
Aircraft Data

This chapter considers the modeling of RPAS/Aircraft data transmission via channels based on IEEE 802.16 standard. RPAS communication channel with a fading was analyzed using original model. Dependencies of a SNR in ground receiver on a SNR in downlink for different types of RPAS amplifier nonlinearity were obtained. Signals constellations of received signals were compared for different Doppler shifts. The influence of the aircraft transmitter nonlinearity for different types of fading in the channel was studied using “80216dstbc Rayleigh,” “80216dstbc Rician,” “80216d Rayleigh,” and “80216d Rician” models. The possibility of the nonlinearity correction using pre-distortion was revealed. The impact of space-time diversity (MISO 2x1) for different types of fading in the channels was investigated. The effect of the Doppler's frequency shift on the operation of communication channels was analyzed.

scholarly journals Mechanics unlocks the morphogenetic puzzle of interlocking bivalved shells

2019 ◽  
Vol 117 (1) ◽  
pp. 43-51 ◽  
Author(s):  
Derek E. Moulton ◽  
Alain Goriely ◽  
Régis Chirat
Keyword(s):  
Pattern Formation ◽  
Growth Process ◽  
Shell Growth ◽  
Point Of View ◽  
Mathematical Framework ◽  
Bivalve Mollusks ◽  
Modeling Framework ◽  
Mechanistic Basis ◽  
Developmental Point ◽  
Rigid Shell

Brachiopods and mollusks are 2 shell-bearing phyla that diverged from a common shell-less ancestor more than 540 million years ago. Brachiopods and bivalve mollusks have also convergently evolved a bivalved shell that displays an apparently mundane, yet striking feature from a developmental point of view: When the shell is closed, the 2 valve edges meet each other in a commissure that forms a continuum with no gaps or overlaps despite the fact that each valve, secreted by 2 mantle lobes, may present antisymmetric ornamental patterns of varying regularity and size. Interlocking is maintained throughout the entirety of development, even when the shell edge exhibits significant irregularity due to injury or other environmental influences, which suggests a dynamic physical process of pattern formation that cannot be genetically specified. Here, we derive a mathematical framework, based on the physics of shell growth, to explain how this interlocking pattern is created and regulated by mechanical instabilities. By close consideration of the geometry and mechanics of 2 lobes of the mantle, constrained both by the rigid shell that they secrete and by each other, we uncover the mechanistic basis for the interlocking pattern. Our modeling framework recovers and explains a large diversity of shell forms and highlights how parametric variations in the growth process result in morphological variation. Beyond the basic interlocking mechanism, we also consider the intricate and striking multiscale-patterned edge in certain brachiopods. We show that this pattern can be explained as a secondary instability that matches morphological trends and data.

Subdividing the sprawl: Endogenous segmentation of housing submarkets in expansion areas of Santiago, Chile

2020 ◽  
pp. 239980832094772
Author(s):  
Tomás Cox ◽  
Ricardo Hurtubia
Keyword(s):  
Real Estate ◽  
Latent Class ◽  
Latent Class Models ◽  
Modeling Framework ◽  
Quantitative Studies ◽  
Different Types ◽  
Real Estate Projects ◽  
Socioeconomic Segregation ◽  
Housing Submarkets ◽  
Location Strategies

Urban sprawl is a phenomenon observed in most cities around the globe and especially in Latin America, where it is associated to socioeconomic segregation. In the case of Chile, sprawl has been generally based on large real estate projects. Developers target their projects to different types of consumers, which translates into submarkets with a broad range of housing-unit’s characteristics, but also different location strategies. This heterogeneity has been analyzed and measured in the literature, but quantitative studies have used exogenous or sequential methods to identify submarkets, leading to potential bias in the segmentation. In this paper, we propose an econometric model to measure location drivers for different types of real estate projects that fills this gap. The modeling framework is based on discrete-choice and latent-class models, allowing us to simultaneously identify market segmentations, and their particular location choice preferences, without the need of arbitrary or ex-ante definitions of submarkets. The model is applied to the city of Santiago, Chile. The results reveal two clearly different approaches taken by developers to produce housing, with one submarket of “exclusive” and more sprawling projects, and another submarket of “massive” and more density driven projects. Location strategies are very different between submarkets, reproducing the socio-spatial segregation already observed in the consolidated city.

scholarly journals A mechanistic modeling framework for gas-phase adsorption kinetics and fixed-bed transport

AIChE Journal ◽  
2017 ◽  
Vol 63 (11) ◽  
pp. 5029-5043 ◽  
Author(s):  
Austin P. Ladshaw ◽  
Sotira Yiacoumi ◽  
Ronghong Lin ◽  
Yue Nan ◽  
Lawrence L. Tavlarides ◽  
...  
Keyword(s):  
Gas Phase ◽  
Adsorption Kinetics ◽  
Fixed Bed ◽  
Mechanistic Modeling ◽  
Modeling Framework ◽  
Gas Phase Adsorption

scholarly journals Energy and Uncertainty: Models and Algorithms for Complex Energy Systems

AI Magazine ◽  
2014 ◽  
Vol 35 (3) ◽  
pp. 8-21 ◽  
Author(s):  
Warren Powell
Keyword(s):  
Learning Community ◽  
Value Function ◽  
Optimization Problems ◽  
Energy Systems ◽  
Modeling Framework ◽  
Different Types ◽  
Case Application ◽  
Storage Problem ◽  
Function Approximations ◽  
Uncertainty Models

The problem of controlling energy systems (generation, transmission, storage, investment) introduces a number of optimization problems which need to be solved in the presence of different types of uncertainty. We highlight several of these applications, using a simple energy storage problem as a case application. Using this setting, we describe a modeling framework based around five fundamental dimensions which is more natural than the standard canonical form widely used in the reinforcement learning community. The framework focuses on finding the best policy, where we identify four fundamental classes of policies consisting of policy function approximations (PFAs), cost function approximations (CFAs), policies based on value function approximations (VFAs), and lookahead policies. This organization unifies a number of competing strategies under a common umbrella.

scholarly journals A practical guide to mechanistic systems modeling in biology using a logic-based approach

2020 ◽  
Author(s):  
Anna Niarakis ◽  
Tomáš Helikar
Keyword(s):  
Computational Modeling ◽  
Computational Models ◽  
Regulatory System ◽  
Mechanistic Modeling ◽  
Lac Operon ◽  
Modeling Framework ◽  
Validation Criteria ◽  
Hands On ◽  
Modeling Software ◽  
Logical Modeling

Abstract Mechanistic computational models enable the study of regulatory mechanisms implicated in various biological processes. These models provide a means to analyze the dynamics of the systems they describe, and to study and interrogate their properties, and provide insights about the emerging behavior of the system in the presence of single or combined perturbations. Aimed at those who are new to computational modeling, we present here a practical hands-on protocol breaking down the process of mechanistic modeling of biological systems in a succession of precise steps. The protocol provides a framework that includes defining the model scope, choosing validation criteria, selecting the appropriate modeling approach, constructing a model and simulating the model. To ensure broad accessibility of the protocol, we use a logical modeling framework, which presents a lower mathematical barrier of entry, and two easy-to-use and popular modeling software tools: Cell Collective and GINsim. The complete modeling workflow is applied to a well-studied and familiar biological process—the lac operon regulatory system. The protocol can be completed by users with little to no prior computational modeling experience approximately within 3 h.

scholarly journals Synaptic Dynamics as Convolutional Units

2020 ◽  
Author(s):  
Julian Rossbroich ◽  
Daniel Trotter ◽  
Katalin Tóth ◽  
Richard Naud
Keyword(s):  
Neural Networks ◽  
Action Potentials ◽  
Computational Models ◽  
Mathematical Framework ◽  
Challenging Problem ◽  
Maximum Likelihood Approach ◽  
Different Types ◽  
Likelihood Approach ◽  
Synaptic Dynamics ◽  
Nonlinear Operation

AbstractSynaptic dynamics differ markedly across connections and strongly regulate how action potentials are being communicated. To model the range of synaptic dynamics observed in experiments, we develop a flexible mathematical framework based on a linear-nonlinear operation. This model can capture various experimentally observed features of synaptic dynamics and different types of heteroskedasticity. Despite its conceptual simplicity, we show it is more adaptable than previous models. Combined with a standard maximum likelihood approach, synaptic dynamics can be accurately and efficiently characterized using naturalistic stimulation patterns. These results make explicit that synaptic processing bears algorithmic similarities with information processing in convolutional neural networks.Author summaryUnderstanding how information is transmitted relies heavily on knowledge of the underlying regulatory synaptic dynamics. Existing computational models for capturing such dynamics are often either very complex or too restrictive. As a result, effectively capturing the different types of dynamics observed experimentally remains a challenging problem. Here, we propose a mathematically flexible linear-nonlinear model that is capable of efficiently characterizing synaptic dynamics. We demonstrate the ability of this model to capture different features of experimentally observed data.

scholarly journals Modeling and Analyzing Stem-Cell Therapy toward Cancer: Evolutionary Game Theory Perspective

2020 ◽  
Author(s):  
Zahra VEISI ◽  
Heydar KHADEM ◽  
Samin RAVANSHADI
Keyword(s):  
Game Theory ◽  
Stem Cells ◽  
Stem Cell ◽  
Evolutionary Game Theory ◽  
Equilibrium Points ◽  
Evolutionary Game ◽  
Mathematical Framework ◽  
Replicator Equations ◽  
Different Types ◽  
Cancerous Cells

Background: Immunotherapy is a recently developed method of cancer therapy, aiming to strengthen a patient’s immune system in different ways to fight cancer. One of these ways is to add stem cells into the patient’s body. Methods: The study was conducted in Kermanshah, western Iran, 2016-2017. We first modeled the interaction between cancerous and healthy cells using the concept of evolutionary game theory. System dynamics were analyzed employing replicator equations and control theory notions. We categorized the system into separate cases based on the value of the parameters. For cases in which the system converged to undesired equilibrium points, “stem-cell injection” was employed as a therapeutic suggestion. The effect of stem cells on the model was considered by reforming the replicator equations as well as adding some new parameters to the system. Results: By adjusting stem cell-related parameters, the system converged to desired equilibrium points, i.e., points with no or a scanty level of cancerous cells. In addition to the theoretical analysis, our simulation results suggested solutions were effective in eliminating cancerous cells. Conclusion: This model could be applicable to different types of cancer, so we did not restrict it to a specific type of cancer. In fact, we were seeking a flexible mathematical framework that could cover different types of cancer by adjusting the system parameters.

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