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Deep phenotyping is an emerging conceptual paradigm and experimental approach that seeks to measure many aspects of phenotypes and link them to understand the underlying biology. Successful deep phenotyping has mostly been applied in…

定量方法 · 定量生物学 2019-04-03 Nan Xu , Dhaval S. Patel , Hang Lu

Deep phenotyping study has become an emerging field to understand the gene function and the structure of biological networks. For the living animal C. elegans, recent advances in genome-editing tools, microfluidic devices and phenotypic…

定量方法 · 定量生物学 2019-05-08 Nan Xu

Locomotion and gross morphology have been important phenotypes for C. elegans genetics since the inception of the field and remain relevant. In parallel with developments in genome sequencing and editing, phenotyping methods have become…

定量方法 · 定量生物学 2014-07-28 Eviatar I. Yemini , André E. X. Brown

Animal behaviors are sometimes decomposable into discrete, stereotyped elements. In one model, such behaviors are triggered by specific commands; in the extreme case, the discreteness of behavior is traced to the discreteness of action…

神经元与认知 · 定量生物学 2009-12-31 Greg J. Stephens , William S. Ryu , William Bialek

The 1mm roundworm C. elegans is a model organism used in many sub-areas of biology to investigate different types of biological processes. In order to complement the n-vivo analysis with computer-based investigations, several methods have…

定量方法 · 定量生物学 2025-10-02 Nemanja Antonic , Monika Scholz , Aymeric Vellinger , Euphrasie Ramahefarivo , Elio Tuci

C. elegans is commonly used in neuroscience for behaviour analysis because of it's compact nervous system with well-described connectivity. Localizing the animal and distinguishing between its head and tail are important tasks to track the…

计算机视觉与模式识别 · 计算机科学 2020-01-14 Mansi Ranjit Mane , Aniket Anand Deshmukh , Adam J. Iliff

Abrupt changes in behavior can often be associated with changes in underlying behavioral states. When placed off food, the foraging behavior of C. elegans can be described as a change between an initial local-search behavior characterized…

神经元与认知 · 定量生物学 2025-06-03 Andrew Margolis , Andrew Gordus

An organism's ability to move freely is a fundamental behaviour in the animal kingdom. To understand animal locomotion requires a characterisation of the material properties, as well as the biomechanics and physiology. We present a…

生物物理 · 物理学 2017-02-17 Netta Cohen , Thomas Ranner

Animal locomotion is often subject to constraints arising from anatomical/physiological asymmetries. We use the nematode C.~elegans as a minimal model system to ask whether such constraints might shape locomotion patterns optimized during…

Neuromechanics aims to understand the link between an animal's neural activity and its physical behaviors. Recent advances in experimental and machine learning techniques enable simultaneous recordings of neural and locomotion dynamics over…

生物物理 · 物理学 2026-05-06 Alexander E. Cohen , Jörn Dunkel

C. elegans locomotion is composed of switches between forward and reversal states punctuated by turns. This locomotory capability is necessary for the nematode to move towards attractive stimuli, escape noxious chemicals, and explore its…

神经元与认知 · 定量生物学 2025-01-03 Megan Morrison , Lai-Sang Young

Animals adjust their behavioral response to sensory input adaptively depending on past experiences. The flexible brain computation is crucial for survival and is of great interest in neuroscience. The nematode C. elegans modulates its…

神经元与认知 · 定量生物学 2024-02-26 Kevin S. Chen , Anuj K. Sharma , Jonathan W. Pillow , Andrew M. Leifer

Animal behavior is often quantified through subjective, incomplete variables that may mask essential dynamics. Here, we develop a behavioral state space in which the full instantaneous state is smoothly unfolded as a combination of…

神经元与认知 · 定量生物学 2019-12-02 Tosif Ahamed , Antonio Carlos Costa , Greg J. Stephens

The nematode Caenorhabditis elegans (C. elegans) serves as an important model organism in a wide variety of biological studies. In this paper we introduce a pipeline for automated analysis of C. elegans imagery for the purpose of studying…

计算机视觉与模式识别 · 计算机科学 2020-05-12 Linfeng Wang , Shu Kong , Zachary Pincus , Charless Fowlkes

Understanding how genotypes map onto phenotypes, fitness, and eventually organisms is arguably the next major missing piece in a fully predictive theory of evolution. We refer to this generally as the problem of the genotype-phenotype map.…

Given the inner complexity of the human nervous system, insight into the dynamics of brain activity can be gained from understanding smaller and simpler organisms, such as the nematode C. Elegans. The behavioural and structural biology of…

神经元与认知 · 定量生物学 2021-07-15 Gonçalo Mestre , Ruxandra Barbulescu , Arlindo L. Oliveira , L. Miguel Silveira

Animals employ different strategies for relating sensory input to behavioral output to navigate sensory environments, but what strategy to use, when to switch and why remain unclear. In C. elegans, navigation is composed of 'steering' and…

神经元与认知 · 定量生物学 2025-08-04 Kevin S. Chen , Andrew M. Leifer , Jonathan W. Pillow

The nematode Caenorhabditis elegans (C. elegans) is used as a model organism to better understand developmental biology and neurobiology. C. elegans features an invariant cell lineage, which has been catalogued and observed using…

计算机视觉与模式识别 · 计算机科学 2022-08-03 Andrew Lauziere , Ryan Christensen , Hari Shroff

We investigate how locomotory behavior is generated in the brain focusing on the paradigmatic connectome of nematode Caenorhabditis elegans (C. elegans) and on neuronal activity patterns that control forward locomotion. We map the neuronal…

适应与自组织系统 · 物理学 2020-06-17 Thomas Maertens , Eckehard Schöll , Jorge Ruiz , Philipp Hövel

Although extensive behavioral changes often exist between closely related animal species, our understanding of the genetic basis underlying the evolution of behavior has remained limited. Here, we propose a new framework to study behavioral…

种群与进化 · 定量生物学 2020-07-21 Damián G. Hernández , Catalina Rivera , Jessica Cande , Baohua Zhou , David L. Stern , Gordon J. Berman
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