add Kobiela2026a

Author: michaelgutmann

_assets/Refereed.bib

@@ -1138,6 +1138,23 @@ @InProceedings{Chen2025a
   url       = {https://openreview.net/forum?id=FYbe7r0mxu},
 }
 
+@Article{Kobiela2026a,
+  author   = {Michal Kobiela and Diego A. Oyarzún and Michael U. Gutmann},
+  journal  = {Cell Systems},
+  title    = {Risk-averse optimization of genetic circuits under uncertainty},
+  year     = {2026},
+  issn     = {2405-4712},
+  number   = {1},
+  pages    = {101476},
+  volume   = {17},
+  abstract = {Summary
+Engineering biological systems with specified functions requires navigating an extensive design space, which is challenging to achieve with wet-lab experiments alone. To expedite the design process, mathematical modeling is typically employed to predict circuit function in silico ahead of implementation, which, when coupled with computational optimization, can be used to automatically identify promising designs. However, circuit models are inherently inaccurate, which can result in suboptimal or non-functional in vivo performance. To mitigate this, we propose combining Bayesian inference, Thompson sampling, and risk management to find optimal circuit designs. Our approach employs data from non-functional designs to estimate the distribution of model parameters and then employs risk-averse optimization to select design parameters that are expected to perform well, given parameter uncertainty and biomolecular noise. We illustrate the approach by designing adaptation circuits and genetic oscillators using real and simulated data, with models of varied complexity. A record of this paper’s transparent peer review process is included in the supplemental information.},
+  arxiv    = {http://biorxiv.org/content/early/2024/11/13/2024.11.13.623219.abstract},
+  doi      = {https://doi.org/10.1016/j.cels.2025.101476},
+  keywords = {automated design, genetic circuits, synthetic biology, machine learning, uncertainty quantification, Thompson sampling, risk-averse optimization, risk-management, robustness},
+  url      = {https://doi.org/10.1016/j.cels.2025.101476},
+}
+
 @Comment{jabref-meta: databaseType:bibtex;}
 
 @Comment{jabref-meta: fileDirectory-mgutmann-zermatt:../myPapers;}

_assets/UnderReview.bib

@@ -1,17 +1,5 @@
 % Encoding: UTF-8
 
-@Article{Kobiela2024a,
-  author   = {Kobiela, Michal and Oyarzun, Diego A. and Gutmann, Michael U.},
-  journal  = {bioRxiv},
-  title    = {Risk-averse optimization of genetic circuits under uncertainty},
-  year     = {2024},
-  month    = jan,
-  abstract = {Synthetic biology aims to engineer biological systems with specified functions. This requires navigating an extensive design space, which is challenging to achieve with wet lab experiments alone. To expedite the design process, mathematical modelling is typically employed to predict circuit function in silico ahead of implementation, which when coupled with computational optimization can be used to automatically identify promising designs. However, circuit models are inherently inaccurate which can result in sub-optimal or non-functional in vivo performance. To mitigate this issue, here we propose to combine Bayesian inference, Thompson sampling, and risk management to find optimal circuit designs. Our approach employs data from non-functional designs to estimate the distribution of the model parameters and then employs risk-averse optimization to select design parameters that are expected to perform well given parameter uncertainty and biomolecular noise. We illustrate the approach by designing robust adaptation circuits and genetic oscillators with a prescribed frequency. The proposed approach provides a novel methodology for the design of robust genetic circuitry.Competing Interest StatementThe authors have declared no competing interest.},
-  arxiv    = {http://biorxiv.org/content/early/2024/11/13/2024.11.13.623219.abstract},
-  doi      = {10.1101/2024.11.13.623219},
-  url      = {http://biorxiv.org/content/early/2024/11/13/2024.11.13.623219.abstract},
-}
-
 @Article{Kelly2025,
   author   = {Ryan P. Kelly and David J. Warne and David T. Frazier and David J. Nott and Michael U. Gutmann and Christopher Drovandi},
   journal  = {arXiv:2503.12315},

index.md

@@ -19,8 +19,7 @@ A short biography is available [here](bio/index.html).
 
 ## Recent papers
 
+* Cell Systems: Risk-averse optimization of genetic circuits under uncertainty [[link]](publications/index.html#Kobiela2026a)
 * NeurIPS: Neural Mutual Information Estimation with Vector Copulas [[link]](publications/index.html#Chen2025a)
 * arXiv: CFMI: Flow Matching for Missing Data Imputation [[link]](publications/index.html#Simkus2025a)
 * arXiv: Simulation-based Bayesian inference under model misspecification [[link]](publications/index.html#Kelly2025a)
-* BioRxiv: Risk-averse optimization of genetic circuits under uncertainty [[link]](publications/index.html#Kobiela2024a)
-* TMLR: Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families [[link]](publications/index.html#Simkus2024a)