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Modeling Mechanical and Biological Systems: Cell Classification and Mechanical Behavior of Biologically Inspired Hydrogel Systems
Modeling Mechanical and Biological Systems: Cell Classification and Mechanical Behavior of Biologically Inspired Hydrogel Systems
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151044
- ISBN
- 9798382757667
- DDC
- 660
- 서명/저자
- Modeling Mechanical and Biological Systems: Cell Classification and Mechanical Behavior of Biologically Inspired Hydrogel Systems
- 발행사항
- [Sl] : Northwestern University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 156 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
- 주기사항
- Advisor: Schatz, George.
- 학위논문주기
- Thesis (Ph.D.)--Northwestern University, 2024.
- 초록/해제
- 요약In the current rapidly evolving technological landscape, many computational frameworks and algorithms that seemed impossible in the past are now feasible. Machine learning, as one of the most popular technology trends, owes its advancement to the latest developments in computational power. Early and accurate detection and characterization of cancer remain a critical hurdle in improving patient outcomes. Machine learning has the power to address this need, but effective clinical implementation remains a significant challenge. In the chapter 2, a feature extraction-based machine learning algorithm is presented that integrates both the spatial distribution of the actin cytoskeleton and the overall morphology of single cells to successfully discriminate between cancer and non-cancerous cell lines.In chapters 3 and 4, an autoregulatory hydrogel system which combines wrinkle-patterned hydrogels with plasmonic nanoparticle (NP) lattices is studied. So far, autoregulatory systems lack the flexibility to extend their regulating feedback loop over a broad range of timescales. By tuning the degree of photothermal heating, the timescale of the regulatory cycle could be programmed. The static and dynamic thermo-mechanical behavior of the bilayer structure using different mechanical models (linear elastic vs viscoelastic) are studied. In chapter 5, multiscale simulations are performed to explore the mechanical behavior of tissue-like synthetic starch-hydrogel systems. The goal of this project is to engineer microstructural features to mimic the mechanical properties of soft tissues.In chapter 6, ion diffusion in stacked planar nano-membranes using Poisson-Nernst-Planck (PNP) theory are studied both statically and dynamically. The electric double layer generated as a result of surface functionalization is also incorporated in our model. Several parameters are quantified such as concentration gradient, electric potential and ion fluxes that are hard to measure experimentally.
- 일반주제명
- Chemical engineering
- 일반주제명
- Oncology
- 일반주제명
- Nanoscience
- 키워드
- Machine learning
- 키워드
- Nanoparticle
- 키워드
- Cancer
- 키워드
- Hydrogel system
- 기타저자
- Northwestern University Chemical and Biological Engineering
- 기본자료저록
- Dissertations Abstracts International. 85-11B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798382757667
■035 ▼a(MiAaPQ)AAI31140334
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a660
■1001 ▼aMousavikhamene, Zeynab.▼0(orcid)0000-0002-2125-2501
■24510▼aModeling Mechanical and Biological Systems: Cell Classification and Mechanical Behavior of Biologically Inspired Hydrogel Systems
■260 ▼a[Sl]▼bNorthwestern University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a156 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-11, Section: B.
■500 ▼aAdvisor: Schatz, George.
■5021 ▼aThesis (Ph.D.)--Northwestern University, 2024.
■520 ▼aIn the current rapidly evolving technological landscape, many computational frameworks and algorithms that seemed impossible in the past are now feasible. Machine learning, as one of the most popular technology trends, owes its advancement to the latest developments in computational power. Early and accurate detection and characterization of cancer remain a critical hurdle in improving patient outcomes. Machine learning has the power to address this need, but effective clinical implementation remains a significant challenge. In the chapter 2, a feature extraction-based machine learning algorithm is presented that integrates both the spatial distribution of the actin cytoskeleton and the overall morphology of single cells to successfully discriminate between cancer and non-cancerous cell lines.In chapters 3 and 4, an autoregulatory hydrogel system which combines wrinkle-patterned hydrogels with plasmonic nanoparticle (NP) lattices is studied. So far, autoregulatory systems lack the flexibility to extend their regulating feedback loop over a broad range of timescales. By tuning the degree of photothermal heating, the timescale of the regulatory cycle could be programmed. The static and dynamic thermo-mechanical behavior of the bilayer structure using different mechanical models (linear elastic vs viscoelastic) are studied. In chapter 5, multiscale simulations are performed to explore the mechanical behavior of tissue-like synthetic starch-hydrogel systems. The goal of this project is to engineer microstructural features to mimic the mechanical properties of soft tissues.In chapter 6, ion diffusion in stacked planar nano-membranes using Poisson-Nernst-Planck (PNP) theory are studied both statically and dynamically. The electric double layer generated as a result of surface functionalization is also incorporated in our model. Several parameters are quantified such as concentration gradient, electric potential and ion fluxes that are hard to measure experimentally.
■590 ▼aSchool code: 0163.
■650 4▼aChemical engineering
■650 4▼aOncology
■650 4▼aNanoscience
■653 ▼aMachine learning
■653 ▼aNanoparticle
■653 ▼aActin cytoskeleton
■653 ▼aCancer
■653 ▼aHydrogel system
■690 ▼a0542
■690 ▼a0565
■690 ▼a0992
■71020▼aNorthwestern University▼bChemical and Biological Engineering.
■7730 ▼tDissertations Abstracts International▼g85-11B.
■790 ▼a0163
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160580▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
