scholarly journals Shaping the Organ: A Biologist Guide to Quantitative Models of Plant Morphogenesis

2021 ◽  
Vol 12 ◽  
Author(s):  
Marco Marconi ◽  
Krzysztof Wabnik
Keyword(s):  
Computational Models ◽  
Large Body ◽  
Pattern Generation ◽  
Computational Techniques ◽  
Mechanical Stimuli ◽  
Organ Morphogenesis ◽  
Shape Acquisition ◽  
Undifferentiated Cells ◽  
Shape Complexity ◽  
User Friendly

Organ morphogenesis is the process of shape acquisition initiated with a small reservoir of undifferentiated cells. In plants, morphogenesis is a complex endeavor that comprises a large number of interacting elements, including mechanical stimuli, biochemical signaling, and genetic prerequisites. Because of the large body of data being produced by modern laboratories, solving this complexity requires the application of computational techniques and analyses. In the last two decades, computational models combined with wet-lab experiments have advanced our understanding of plant organ morphogenesis. Here, we provide a comprehensive review of the most important achievements in the field of computational plant morphodynamics. We present a brief history from the earliest attempts to describe plant forms using algorithmic pattern generation to the evolution of quantitative cell-based models fueled by increasing computational power. We then provide an overview of the most common types of “digital plant” paradigms, and demonstrate how models benefit from diverse techniques used to describe cell growth mechanics. Finally, we highlight the development of computational frameworks designed to resolve organ shape complexity through integration of mechanical, biochemical, and genetic cues into a quantitative standardized and user-friendly environment.

Genetic Interactions Effects of Cardiovascular Disorder Using Computational Models: A Review

2020 ◽  
Vol 9 (3) ◽  
pp. 177-191
Author(s):  
Sridharan Priya ◽  
Radha K. Manavalan
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Cardiovascular Diseases ◽  
Computational Models ◽  
Genetic Interaction ◽  
Association Studies ◽  
Genetic Interactions ◽  
Computational Techniques ◽  
Genome Wide Association Studies ◽  
Artery Disease

Background: The diseases in the heart and blood vessels such as heart attack, Coronary Artery Disease, Myocardial Infarction (MI), High Blood Pressure, and Obesity, are generally referred to as Cardiovascular Diseases (CVD). The risk factors of CVD include gender, age, cholesterol/ LDL, family history, hypertension, smoking, and genetic and environmental factors. Genome- Wide Association Studies (GWAS) focus on identifying the genetic interactions and genetic architectures of CVD. Objective: Genetic interactions or Epistasis infer the interactions between two or more genes where one gene masks the traits of another gene and increases the susceptibility of CVD. To identify the Epistasis relationship through biological or laboratory methods needs an enormous workforce and more cost. Hence, this paper presents the review of various statistical and Machine learning approaches so far proposed to detect genetic interaction effects for the identification of various Cardiovascular diseases such as Coronary Artery Disease (CAD), MI, Hypertension, HDL and Lipid phenotypes data, and Body Mass Index dataset. Conclusion: This study reveals that various computational models identified the candidate genes such as AGT, PAI-1, ACE, PTPN22, MTHR, FAM107B, ZNF107, PON1, PON2, GTF2E1, ADGRB3, and FTO, which play a major role in genetic interactions for the causes of CVDs. The benefits, limitations, and issues of the various computational techniques for the evolution of epistasis responsible for cardiovascular diseases are exhibited.

Modeling of Steelmaking Processes

2016 ◽  
pp. 369-421
Author(s):  
Seppo Louhenkilpi ◽  
Subhas Ganguly
Keyword(s):  
Statistical Learning ◽  
Quantum Chemical ◽  
High Performance ◽  
Process Model ◽  
Computational Models ◽  
Computational Techniques ◽  
Computational Process ◽  
Steel Making ◽  
Steel Converter ◽  
Steelmaking Technology

In the field of experiment, theory, modeling and simulation, the most noteworthy progressions applicable to steelmaking technology have been closely linked with the emergence of more powerful computing tools, advances in needful software's and algorithms design, and to a lesser degree, with the development of emerging computing theory. These have enabled the integration of several different types of computational techniques (for example, quantum chemical, and molecular dynamics, DFT, FEM, Soft computing, statistical learning etc., to name a few) to provide high-performance simulations of steelmaking processes based on emerging computational models and theories. This chapter overviews the general steps and concepts for developing a computational process model including few exercises in the area of steel making. The various sections of the chapter aim to describe how to developed models for various issues related to steelmaking processes and to simulate a physical process starts with the process fundaments. The examples include steel converter, tank vacuum degassing, and continuous casting, etc.

iRNAD: a computational tool for identifying D modification sites in RNA sequence

2019 ◽  
Vol 35 (23) ◽  
pp. 4922-4929 ◽  
Author(s):  
Zhao-Chun Xu ◽  
Peng-Mian Feng ◽  
Hui Yang ◽  
Wang-Ren Qiu ◽  
Wei Chen ◽  
...  
Keyword(s):  
Computational Models ◽  
Operating Characteristic ◽  
Cross Validation ◽  
Characteristic Curve ◽  
Support Vector ◽  
Final Model ◽  
Rna Sequence ◽  
Functional Roles ◽  
Proposed Model ◽  
User Friendly

Abstract Motivation Dihydrouridine (D) is a common RNA post-transcriptional modification found in eukaryotes, bacteria and a few archaea. The modification can promote the conformational flexibility of individual nucleotide bases. And its levels are increased in cancerous tissues. Therefore, it is necessary to detect D in RNA for further understanding its functional roles. Since wet-experimental techniques for the aim are time-consuming and laborious, it is urgent to develop computational models to identify D modification sites in RNA. Results We constructed a predictor, called iRNAD, for identifying D modification sites in RNA sequence. In this predictor, the RNA samples derived from five species were encoded by nucleotide chemical property and nucleotide density. Support vector machine was utilized to perform the classification. The final model could produce the overall accuracy of 96.18% with the area under the receiver operating characteristic curve of 0.9839 in jackknife cross-validation test. Furthermore, we performed a series of validations from several aspects and demonstrated the robustness and reliability of the proposed model. Availability and implementation A user-friendly web-server called iRNAD can be freely accessible at http://lin-group.cn/server/iRNAD, which will provide convenience and guide to users for further studying D modification.

scholarly journals Reconfiguration of the Pontomedullary Respiratory Network: A Computational Modeling Study With Coordinated In Vivo Experiments

2008 ◽  
Vol 100 (4) ◽  
pp. 1770-1799 ◽  
Author(s):  
I. A. Rybak ◽  
R. O'Connor ◽  
A. Ross ◽  
N. A. Shevtsova ◽  
S. C. Nuding ◽  
...  
Keyword(s):  
Computational Models ◽  
Ad Hoc ◽  
Large Body ◽  
Network Activity ◽  
Respiratory Pattern ◽  
Respiratory Neurons ◽  
Phase Switching ◽  
In Vivo Experiments ◽  
Respiratory Network

A large body of data suggests that the pontine respiratory group (PRG) is involved in respiratory phase-switching and the reconfiguration of the brain stem respiratory network. However, connectivity between the PRG and ventral respiratory column (VRC) in computational models has been largely ad hoc. We developed a network model with PRG-VRC connectivity inferred from coordinated in vivo experiments. Neurons were modeled in the “integrate-and-fire” style; some neurons had pacemaker properties derived from the model of Breen et al. We recapitulated earlier modeling results, including reproduction of activity profiles of different respiratory neurons and motor outputs, and their changes under different conditions (vagotomy, pontine lesions, etc.). The model also reproduced characteristic changes in neuronal and motor patterns observed in vivo during fictive cough and during hypoxia in non-rapid eye movement sleep. Our simulations suggested possible mechanisms for respiratory pattern reorganization during these behaviors. The model predicted that network- and pacemaker-generated rhythms could be co-expressed during the transition from gasping to eupnea, producing a combined “burst-ramp” pattern of phrenic discharges. To test this prediction, phrenic activity and multiple single neuron spike trains were monitored in vagotomized, decerebrate, immobilized, thoracotomized, and artificially ventilated cats during hypoxia and recovery. In most experiments, phrenic discharge patterns during recovery from hypoxia were similar to those predicted by the model. We conclude that under certain conditions, e.g., during recovery from severe brain hypoxia, components of a distributed network activity present during eupnea can be co-expressed with gasp patterns generated by a distinct, functionally “simplified” mechanism.

scholarly journals DNA6mA-MINT: DNA-6mA Modification Identification Neural Tool

Genes ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 898 ◽  
Author(s):  
Mobeen Ur Rehman ◽  
Kil To Chong
Keyword(s):  
Computational Models ◽  
Short Term Memory ◽  
Binary Sequence ◽  
The Neural Network ◽  
Proposed Model ◽  
Time Parameters ◽  
Long Short Term Memory ◽  
High Level ◽  
User Friendly ◽  
Fold Cross Validation

DNA N6-methyladenine (6mA) is part of numerous biological processes including DNA repair, DNA replication, and DNA transcription. The 6mA modification sites hold a great impact when their biological function is under consideration. Research in biochemical experiments for this purpose is carried out and they have demonstrated good results. However, they proved not to be a practical solution when accessed under cost and time parameters. This led researchers to develop computational models to fulfill the requirement of modification identification. In consensus, we have developed a computational model recommended by Chou’s 5-steps rule. The Neural Network (NN) model uses convolution layers to extract the high-level features from the encoded binary sequence. These extracted features were given an optimal interpretation by using a Long Short-Term Memory (LSTM) layer. The proposed architecture showed higher performance compared to state-of-the-art techniques. The proposed model is evaluated on Mus musculus, Rice, and “Combined-species” genomes with 5- and 10-fold cross-validation. Further, with access to a user-friendly web server, publicly available can be accessed freely.

scholarly journals An Overview of In Vitro, In Vivo, and Computational Techniques for Cancer-Associated Angiogenesis Studies

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Heshu Sulaiman Rahman ◽  
Bee Ling Tan ◽  
Hemn Hassan Othman ◽  
Max Stanley Chartrand ◽  
Yashwant Pathak ◽  
...  
Keyword(s):  
Computational Models ◽  
Therapeutic Agents ◽  
Computational Techniques ◽  
Macroscopic Scale ◽  
Angiogenic Response ◽  
Physiological Processes ◽  
Alternative Approach ◽  
Standard Models

Angiogenesis is a crucial area in scientific research because it involves many important physiological and pathological processes. Indeed, angiogenesis is critical for normal physiological processes, including wound healing and embryonic development, as well as being a component of many disorders, such as rheumatoid arthritis, obesity, and diabetic retinopathies. Investigations of angiogenic mechanisms require assays that can activate the critical steps of angiogenesis as well as provide a tool for assessing the efficacy of therapeutic agents. Thus, angiogenesis assays are key tools for studying the mechanisms of angiogenesis and identifying the potential therapeutic strategies to modulate neovascularization. However, the regulation of angiogenesis is highly complex and not fully understood. Difficulties in assessing the regulators of angiogenic response have necessitated the development of an alternative approach. In this paper, we review the standard models for the study of tumor angiogenesis on the macroscopic scale that include in vitro, in vivo, and computational models. We also highlight the differences in several modeling approaches and describe key advances in understanding the computational models that contributed to the knowledge base of the field.

High Fidelity Computational Model of Bone Remodeling Cellular Mechanisms

2010 ◽  
Author(s):  
Charles L. Penninger ◽  
Andrés Tovar ◽  
Glen L. Niebur ◽  
John E. Renaud
Keyword(s):  
Bone Remodeling ◽  
Computational Models ◽  
Bone Adaptation ◽  
Daily Activities ◽  
High Fidelity ◽  
Mechanical Stimuli ◽  
Cellular Activity ◽  
Cellular Mechanisms ◽  
Mechanical Loads ◽  
Metabolic Activities

One of the most intriguing aspects of bone is its ability to grow, repair damage, adapt to mechanical loads, and maintain mineral homeostasis [1]. It is generally accepted that bone adaptation occurs in response to the mechanical demands of our daily activities; moreover, strain and microdamage have been implicated as potential stimuli that regulate bone remodeling [2]. Computational models have been used to simulate remodeling in an attempt to better understand the metabolic activities which possess the key information of how this process is carried out [3]. At present, the connection between the cellular activity of remodeling and the applied mechanical stimuli is not fully understood. Only a few mathematical models have been formulated to characterize the remolding process in terms of the cellular mechanisms that occur [4,5].

scholarly journals Computational Models of Neuromodulation

1998 ◽  
Vol 10 (4) ◽  
pp. 771-805 ◽  
Author(s):  
Jean-Marc Fellous ◽  
Christiane Linster
Keyword(s):  
Sensory Processing ◽  
Information Integration ◽  
Computational Models ◽  
Neural Substrates ◽  
Pattern Generation ◽  
Neural Models ◽  
Functional Roles ◽  
Modeling Studies ◽  
And Control

Computational modeling of neural substrates provides an excellent theoretical framework for the understanding of the computational roles of neuromodulation. In this review, we illustrate, with a large number of modeling studies, the specific computations performed by neuromodulation in the context of various neural models of invertebrate and vertebrate preparations. We base our characterization of neuromodulations on their computational and functional roles rather than on anatomical or chemical criteria. We review the main framework in which neuromodulation has been studied theoretically (central pattern generation and oscillations, sensory processing, memory and information integration). Finally, we present a detailed mathematical overview of how neuromodulation has been implemented at the single cell and network levels in modeling studies. Overall, neuromodulation is found to increase and control computational complexity.

IN SEARCH OF THE PROTEIN NATIVE STATE WITH A PROBABILISTIC SAMPLING APPROACH

2011 ◽  
Vol 09 (03) ◽  
pp. 383-398 ◽  
Author(s):  
BRIAN OLSON ◽  
KEVIN MOLLOY ◽  
AMARDA SHEHU
Keyword(s):  
Structure Prediction ◽  
Computational Models ◽  
Three Dimensional ◽  
Structural Diversity ◽  
Search Space ◽  
Conformational Space ◽  
Conformational Search ◽  
Dimensional Structure ◽  
Computational Techniques ◽  
Native State

The three-dimensional structure of a protein is a key determinant of its biological function. Given the cost and time required to acquire this structure through experimental means, computational models are necessary to complement wet-lab efforts. Many computational techniques exist for navigating the high-dimensional protein conformational search space, which is explored for low-energy conformations that comprise a protein's native states. This work proposes two strategies to enhance the sampling of conformations near the native state. An enhanced fragment library with greater structural diversity is used to expand the search space in the context of fragment-based assembly. To manage the increased complexity of the search space, only a representative subset of the sampled conformations is retained to further guide the search towards the native state. Our results make the case that these two strategies greatly enhance the sampling of the conformational space near the native state. A detailed comparative analysis shows that our approach performs as well as state-of-the-art ab initio structure prediction protocols.

scholarly journals Evolution of body size and wing shape trade-offs in arsenurine silkmoths

2020 ◽  
Author(s):  
Chris A. Hamilton ◽  
Nathalie Winiger ◽  
Juliette J. Rubin ◽  
Jesse Breinholt ◽  
Rodolphe Rougerie ◽  
...  
Keyword(s):  
Body Size ◽  
Phylogenetic Signal ◽  
Large Body ◽  
Wing Shape ◽  
Phylogenetic Networks ◽  
Biological Research ◽  
Target Capture ◽  
Trade Offs ◽  
In The Wild ◽  
Shape Complexity

AbstractOne of the key objectives in biological research is understanding how evolutionary processes have produced Earth’s biodiversity. These processes have led to a vast diversity of wing shapes in insects; an unanswered question especially pronounced in moths. As one of the major predators of nocturnal moths, bats are thought to have been involved in a long evolutionary arms race with their prey. In response, moths are thought to have evolved many counter strategies, such as diverse wing shapes and large body sizes. However, the tradeoffs between body size and wing shape are not well understood. Here we examined the evolution of wing shape in the wild silkmoth subfamily Arsenurinae (Saturniidae). By using phylogenomics and geometric morphometrics, we established the framework to evaluate potential evolutionary relationships between body size and wing shape. The phylogeny was inferred based on 781 loci from target capture data of 42 arsenurine species representing all 10 recognized genera.We found there are evolutionary trade-offs between body size, wing shape, and the interaction of fore- and hindwing shape. Namely, body size decreases with increasing hindwing length, but increases as forewing shape becomes more complex. Additionally, hindwing shape has a significant effect on forewing shape complexity. The complex wing shapes that make Arsenurinae, and silkmoths as a whole, so charismatic are likely driven by the strong forces of natural selection and genomic constraints.One other important outcome was discovering within our data one of the most vexing problems in phylogenetic inference – a region of a tree that possesses short branches and no “support” for relationships (i.e., a polytomy). These parts of the Tree of Life are often some of the most interesting from an evolutionary standpoint. To investigate this problem, we used reciprocal illumination to determine the most probable generic relationships within the Arsenurinae by inspecting differing phylogenetic inferences, alternative support values, quartets, and phylogenetic networks to reveal hidden phylogenetic signal.

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