FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2900657508> ?p ?o ?g. }

• W2900657508 abstract "Synthetic biology is defined as the engineering of biology: the deliberate (re)design and construction of novel biological and biologically based parts, devices and systems to perform new functions for useful purposes, that draws on principles elucidated from biology and engineering. Methods and tools are needed to facilitate fast, reproducible and predictable construction of biological systems from sets of biological components. This thesis raises multi-objective optimization as the proper framework to deal with common problems arising in rational design and optimal tuning of synthetic gene circuits. Using a classical systems engineering approach, the thesis mainly addresses: i) synthetic gene circuit modeling based on first principles, ii) model parameters estimation from experimental data and iii) model-based tuning to achieve desired circuit performance. Two gene synthetic circuits of different nature and with different goals and inherent problems have been used throughout the thesis: an Incoherent type 1 feedforward circuit (I1-FFL) that exhibits the important biological property of adaptation, and a Quorum sensing/Feedback circuit (QS/Fb) comprising two intertwined feedback loops -an intracellular one and a cell-to-cell communication-based one-- designed to regulate the mean expression level of a protein of interest while minimizing its variance across the population of cells. Both circuits have been analyzed in silico and implemented in vivo. In both cases, circuit modeling based on first principles has been carried out. Then, special attention is paid to illustrate how to obtain reduced order models amenable for parameters estimation yet keeping biological significance. Model parameters estimation from experimental data is considered in different scenarios, both using deterministic and stochastic models. For the I1-FFL circuit, deterministic models are considered. In this case, the thesis raises ensemble modeling using multi-objective optimization to perform model parameters estimation under scenarios with incomplete model structure (unmodeled dynamics). For the QS/Fb gene circuit, a feedback controlled structure, the lack of excitability of the signals is the problem addressed. The thesis proposes a two-stage estimation methodology using stochastic models. The methodology allows using population averaged time-course data and steady state distribution measurements at the single-cell level. Model-based circuit tuning to achieve desired circuit performance is also addressed using multi-objective optimization. First, for the QS/Fb feedback control circuit, a complete stochastic analysis is performed. Here, the thesis addresses how to correctly take into account both intrinsic and extrinsic noise, the two main sources of noise in gene synthetic circuits. The trade-off between both sources of noise, and the role played by in the intracellular single-cell feedback loop and the extracellular population-wide feedback is analyzed. The main conclusion being that the complex interplay between both feedback channels compel the use of multi-objective optimization for proper tuning of the circuit to achieve desired performance. Thus, the thesis wraps up all the previous results and uses them to address circuit tuning for desired performance. Here, besides the proper use of multi-objective optimization tools, the main concern is how to derive guidelines for circuit parameters tuning in silico that can realistically be applied in vivo in a standard laboratory. Thus, as an alternative to classical parameters sensitivity analysis, the thesis proposes the use of clustering techniques along the optimal Pareto fronts relating the performance trade-offs with regions in the circuits parameters space." @default.
• W2900657508 created "2018-11-29" @default.
• W2900657508 creator A5048076494 @default.
• W2900657508 date "2018-01-01" @default.
• W2900657508 modified "2023-09-26" @default.
• W2900657508 title "A systems engineering approach to model, tune and test synthetic gene circuits" @default.
• W2900657508 hasPublicationYear "2018" @default.
• W2900657508 type Work @default.
• W2900657508 sameAs 2900657508 @default.
• W2900657508 citedByCount "0" @default.
• W2900657508 crossrefType "journal-article" @default.
• W2900657508 hasAuthorship W2900657508A5048076494 @default.
• W2900657508 hasConcept C119599485 @default.
• W2900657508 hasConcept C127413603 @default.
• W2900657508 hasConcept C133731056 @default.
• W2900657508 hasConcept C134146338 @default.
• W2900657508 hasConcept C152662350 @default.
• W2900657508 hasConcept C191908910 @default.
• W2900657508 hasConcept C41008148 @default.
• W2900657508 hasConcept C70721500 @default.
• W2900657508 hasConcept C86803240 @default.
• W2900657508 hasConceptScore W2900657508C119599485 @default.
• W2900657508 hasConceptScore W2900657508C127413603 @default.
• W2900657508 hasConceptScore W2900657508C133731056 @default.
• W2900657508 hasConceptScore W2900657508C134146338 @default.
• W2900657508 hasConceptScore W2900657508C152662350 @default.
• W2900657508 hasConceptScore W2900657508C191908910 @default.
• W2900657508 hasConceptScore W2900657508C41008148 @default.
• W2900657508 hasConceptScore W2900657508C70721500 @default.
• W2900657508 hasConceptScore W2900657508C86803240 @default.
• W2900657508 hasLocation W29006575081 @default.
• W2900657508 hasOpenAccess W2900657508 @default.
• W2900657508 hasPrimaryLocation W29006575081 @default.
• W2900657508 hasRelatedWork W1505886598 @default.
• W2900657508 hasRelatedWork W2109450588 @default.
• W2900657508 hasRelatedWork W2150016961 @default.
• W2900657508 hasRelatedWork W2216330956 @default.
• W2900657508 hasRelatedWork W2295074711 @default.
• W2900657508 hasRelatedWork W2525396724 @default.
• W2900657508 hasRelatedWork W2528599819 @default.
• W2900657508 hasRelatedWork W2571166421 @default.
• W2900657508 hasRelatedWork W2584421149 @default.
• W2900657508 hasRelatedWork W2613590922 @default.
• W2900657508 hasRelatedWork W2766191773 @default.
• W2900657508 hasRelatedWork W2797414813 @default.
• W2900657508 hasRelatedWork W2883882445 @default.
• W2900657508 hasRelatedWork W2890366991 @default.
• W2900657508 hasRelatedWork W2913355224 @default.
• W2900657508 hasRelatedWork W2953273605 @default.
• W2900657508 hasRelatedWork W3006113212 @default.
• W2900657508 hasRelatedWork W3100480345 @default.
• W2900657508 hasRelatedWork W9131880 @default.
• W2900657508 hasRelatedWork W2512653673 @default.
• W2900657508 isParatext "false" @default.
• W2900657508 isRetracted "false" @default.
• W2900657508 magId "2900657508" @default.
• W2900657508 workType "article" @default.