Deriving Time-varying Cellular Motility Parameters via Wavelet Analysis
Cell migration is an indispensable physiological and pathological process for normal tissue development and cancer metastasis, which is greatly regulated by intracellular signal pathways and extracellular microenvironment (ECM). However, there is a lack of adequate tools to analyze the time-varying...
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| creator | Liu, Yanping Jiao, Yang Li, Guoqiang Wang, Gao Yao, Jingru Chen, Guo Lou, Silong Shuai, Jianwei Liu, Liyu |
| description | Cell migration is an indispensable physiological and pathological process for
normal tissue development and cancer metastasis, which is greatly regulated by
intracellular signal pathways and extracellular microenvironment (ECM).
However, there is a lack of adequate tools to analyze the time-varying cell
migration characteristics because of the effects of some factors, i.e., the ECM
including the time-dependent local stiffness due to microstructural remodeling
by migrating cells. Here, we develop an approach to derive the time-dependent
motility parameters from cellular trajectories, based on the time-varying
persistent random walk model. In particular, we employ the wavelet denoising
and wavelet transform to investigate cell migration velocities and obtain the
wavelet power spectrum. The time-dependent motility parameters are subsequently
derived via Lorentzian power spectrum. Our analysis shows that the combination
of wavelet denoising, wavelet transform and Lorentzian power spectrum provides
a powerful tool to derive accurately the time-dependent motility parameters,
which reflects the time-varying microenvironment characteristics to some
extent. |
| doi_str_mv | 10.48550/arxiv.2010.12752 |
| format | Article |
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normal tissue development and cancer metastasis, which is greatly regulated by
intracellular signal pathways and extracellular microenvironment (ECM).
However, there is a lack of adequate tools to analyze the time-varying cell
migration characteristics because of the effects of some factors, i.e., the ECM
including the time-dependent local stiffness due to microstructural remodeling
by migrating cells. Here, we develop an approach to derive the time-dependent
motility parameters from cellular trajectories, based on the time-varying
persistent random walk model. In particular, we employ the wavelet denoising
and wavelet transform to investigate cell migration velocities and obtain the
wavelet power spectrum. The time-dependent motility parameters are subsequently
derived via Lorentzian power spectrum. Our analysis shows that the combination
of wavelet denoising, wavelet transform and Lorentzian power spectrum provides
a powerful tool to derive accurately the time-dependent motility parameters,
which reflects the time-varying microenvironment characteristics to some
extent.</description><identifier>DOI: 10.48550/arxiv.2010.12752</identifier><language>eng</language><subject>Physics - Biological Physics</subject><creationdate>2020-10</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2010.12752$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2010.12752$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Liu, Yanping</creatorcontrib><creatorcontrib>Jiao, Yang</creatorcontrib><creatorcontrib>Li, Guoqiang</creatorcontrib><creatorcontrib>Wang, Gao</creatorcontrib><creatorcontrib>Yao, Jingru</creatorcontrib><creatorcontrib>Chen, Guo</creatorcontrib><creatorcontrib>Lou, Silong</creatorcontrib><creatorcontrib>Shuai, Jianwei</creatorcontrib><creatorcontrib>Liu, Liyu</creatorcontrib><title>Deriving Time-varying Cellular Motility Parameters via Wavelet Analysis</title><description>Cell migration is an indispensable physiological and pathological process for
normal tissue development and cancer metastasis, which is greatly regulated by
intracellular signal pathways and extracellular microenvironment (ECM).
However, there is a lack of adequate tools to analyze the time-varying cell
migration characteristics because of the effects of some factors, i.e., the ECM
including the time-dependent local stiffness due to microstructural remodeling
by migrating cells. Here, we develop an approach to derive the time-dependent
motility parameters from cellular trajectories, based on the time-varying
persistent random walk model. In particular, we employ the wavelet denoising
and wavelet transform to investigate cell migration velocities and obtain the
wavelet power spectrum. The time-dependent motility parameters are subsequently
derived via Lorentzian power spectrum. Our analysis shows that the combination
of wavelet denoising, wavelet transform and Lorentzian power spectrum provides
a powerful tool to derive accurately the time-dependent motility parameters,
which reflects the time-varying microenvironment characteristics to some
extent.</description><subject>Physics - Biological Physics</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj7FOwzAURb0woMIHMOEfSHHsZ8ceqwAFqQiGSB2jF-elsuQU5ASL_D1tYbq6dzi6h7G7UqzBai0eMP2EvJbiNJSy0vKabR8phRyOB96EkYqMaTmXmmL8jpj42-ccYpgX_oEJR5opTTwH5HvMFGnmmyPGZQrTDbsaME50-58r1jw_NfVLsXvfvtabXYGmkgV2fWUcOA-uN4a0gh6U6shp45QoAYwVVpMdhO3Ieu9FBRqgdKgl-n5QK3b_h72YtF8pjKfH7dmovRipX1auRhQ</recordid><startdate>20201023</startdate><enddate>20201023</enddate><creator>Liu, Yanping</creator><creator>Jiao, Yang</creator><creator>Li, Guoqiang</creator><creator>Wang, Gao</creator><creator>Yao, Jingru</creator><creator>Chen, Guo</creator><creator>Lou, Silong</creator><creator>Shuai, Jianwei</creator><creator>Liu, Liyu</creator><scope>GOX</scope></search><sort><creationdate>20201023</creationdate><title>Deriving Time-varying Cellular Motility Parameters via Wavelet Analysis</title><author>Liu, Yanping ; Jiao, Yang ; Li, Guoqiang ; Wang, Gao ; Yao, Jingru ; Chen, Guo ; Lou, Silong ; Shuai, Jianwei ; Liu, Liyu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a672-abd76949c49d66e534d433be95693014468085e8f08be8ccc07454419a52acdf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Physics - Biological Physics</topic><toplevel>online_resources</toplevel><creatorcontrib>Liu, Yanping</creatorcontrib><creatorcontrib>Jiao, Yang</creatorcontrib><creatorcontrib>Li, Guoqiang</creatorcontrib><creatorcontrib>Wang, Gao</creatorcontrib><creatorcontrib>Yao, Jingru</creatorcontrib><creatorcontrib>Chen, Guo</creatorcontrib><creatorcontrib>Lou, Silong</creatorcontrib><creatorcontrib>Shuai, Jianwei</creatorcontrib><creatorcontrib>Liu, Liyu</creatorcontrib><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Liu, Yanping</au><au>Jiao, Yang</au><au>Li, Guoqiang</au><au>Wang, Gao</au><au>Yao, Jingru</au><au>Chen, Guo</au><au>Lou, Silong</au><au>Shuai, Jianwei</au><au>Liu, Liyu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Deriving Time-varying Cellular Motility Parameters via Wavelet Analysis</atitle><date>2020-10-23</date><risdate>2020</risdate><abstract>Cell migration is an indispensable physiological and pathological process for
normal tissue development and cancer metastasis, which is greatly regulated by
intracellular signal pathways and extracellular microenvironment (ECM).
However, there is a lack of adequate tools to analyze the time-varying cell
migration characteristics because of the effects of some factors, i.e., the ECM
including the time-dependent local stiffness due to microstructural remodeling
by migrating cells. Here, we develop an approach to derive the time-dependent
motility parameters from cellular trajectories, based on the time-varying
persistent random walk model. In particular, we employ the wavelet denoising
and wavelet transform to investigate cell migration velocities and obtain the
wavelet power spectrum. The time-dependent motility parameters are subsequently
derived via Lorentzian power spectrum. Our analysis shows that the combination
of wavelet denoising, wavelet transform and Lorentzian power spectrum provides
a powerful tool to derive accurately the time-dependent motility parameters,
which reflects the time-varying microenvironment characteristics to some
extent.</abstract><doi>10.48550/arxiv.2010.12752</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Physics - Biological Physics |
| title | Deriving Time-varying Cellular Motility Parameters via Wavelet Analysis |
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