https://homepages.fhv.at/hgb/New-Papers/CMAME07_BS07b.pdf

Robust Optimization - A Comprehensive Survey

Over the past decade, the Rosetta biomolecular modeling suite has informed diverse biological questions and engineering challenges ranging from interpretation of low-resolution structural data to design of nanomaterials, protein therapeutics, and vaccines. Central to Rosetta’s success is the energy function: a model parametrized from small-molecule and X-ray crystal structure data used to approximate the energy associated with each biomolecule conformation. This paper describes the mathematical models and physical concepts that underlie the latest Rosetta energy function, called the Rosetta Energy Function 2015 (REF15). Applying these concepts, we explain how to use Rosetta energies to identify and analyze the features of biomolecular models. Finally, we discuss the latest advances in the energy function that extend its capabilities from soluble proteins to also include membrane proteins, peptides containing noncanonical amino acids, small molecules, carbohydrates, nucleic acids, and other macromolecules.

/pdf/the-rosetta-all-atom-energy-function-for-macromolecular-2dtlzlw8u6.pdf

The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design.

textBACKGROUND: Screening for prostate cancer advances the time of diagnosis
 (lead time) and detects cancers that would not have been diagnosed in the
 absence of screening (overdetection). Both consequences have considerable
 impact on the net benefits of screening. METHODS: We developed simulation
 models based on results of the Rotterdam section of the European
 Randomized Study of Screening for Prostate Cancer (ERSPC), which enrolled
 42,376 men and in which 1498 cases of prostate cancer were identified, and
 on baseline prostate cancer incidence and stage distribution data. The
 models were used to predict mean lead times, overdetection rates, and
 ranges (corresponding to approximate 95% confidence intervals) associated
 with different screening programs. RESULTS: Mean lead times and rates of
 overdetection depended on a man's age at screening. For a single screening
 test at age 55, the estimated mean lead time was 12.3 years (range =
 11.6-14.1 years) and the overdetection rate was 27% (range = 24%-37%); at
 age 75, the estimates were 6.0 years (range = 5.8-6.3 years) and 56%
 (range = 53%-61%), respectively. For a screening program with a 4-year
 screening interval from age 55 to 67, the estimated mean lead time was
 11.2 years (range = 10.8-12.1 years), and the overdetection rate was 48%
 (range = 44%-55%). This screening program raised the lifetime risk of a
 prostate cancer diagnosis from 6.4% to 10.6%, a relative increase of 65%
 (range = 56%-87%). In annual screening from age 55 to 67, the estimated
 overdetection rate was 50% (range = 46%-57%) and the lifetime prostate
 cancer risk was increased by 80% (range = 69%-116%). Extending annual or
 quadrennial screening to the age of 75 would result in at least two cases
 of overdetection for every clinically relevant cancer detected.
 CONCLUSIONS: These model-based lead-time estimates support a prostate
 cancer screening interval of more than 1 year.

/pdf/lead-times-and-overdetection-due-to-prostate-specific-m0qok7a4pu.pdf

Lead times and overdetection due to prostate-specific antigen screening: estimates from the European Randomized Study of Screening for Prostate Cancer.

Over the past decade, the Rosetta biomolecular modeling suite has informed diverse biological questions and engineering challenges ranging from interpretation of low-resolution structural data to design of nanomaterials, protein therapeutics, and vaccines. Central to Rosetta9s success is the energy function: a model parameterized from small molecule and X-ray crystal structure data used to approximate the energy associated with each biomolecule conformation. This paper describes the mathematical models and physical concepts that underlie the latest Rosetta energy function, Aasgard2017. Applying these concepts, we explain how to use Rosetta energies to identify and analyze the features of biomolecular models. Finally, we discuss the latest advances in the energy function that extend capabilities from soluble proteins to also include membrane proteins, peptides containing non-canonical amino acids, carbohydrates, nucleic acids, and other macromolecules.

https://www.biorxiv.org/content/biorxiv/early/2017/02/07/106054.full.pdf

The Rosetta all-atom energy function for macromolecular modeling and design

Journal of Scientific Instruments Editor: Dr H R Lang Vol 28 and Supplement No 1, 1951 Pp xvi + 388 + iii + 80 (London: Institute of Physics, 1951) Bound, £3 12s; unbound, £3

/pdf/a-review-of-scientific-instruments-50cfd4j98s.pdf

A Review of Scientific Instruments

It is pointed out that the Nelder-Mead simplex method rescaling and shrinking steps make it sensitive to random variations in the response function values, and introduce risks of false convergence on stochastic functions. The authors give analytical and empirical evidence that characterizes false convergence on stochastic functions, and discuss several modifications to reduce the likelihood of false convergence. They describe a computational comparison of several modifications when used to optimize simple stochastic functions of two and ten variables. >

Modifications of the Nelder-Mead simplex method for stochastic simulation response optimization

When the Nelder-Mead method is used to optimize the expected response of a stochastic system e.g., an output of a discrete-event simulation model, the simplex-resizing steps of the method introduce risks of inappropriate termination. We give analytical and empirical results describing the performance of Nelder-Mead when it is applied to a response function that incorporates an additive white-noise error, and we use these results to develop new modifications of Nelder-Mead that yield improved estimates of the optimal expected response. Compared to Nelder-Mead, the best performance was obtained by a modified method, RS + S9, in which a the best point in the simplex is reevaluated at each shrink, step and b the simplex is reduced by 10% rather than 50% at each shrink step. In a suite of 18 test problems that were adapted from the MINPACK collection of NETLIB, the expected response at the estimated optimal point obtained by RS + S9 had errors that averaged 15% less than at the original method's estimated optimal point, at an average cost of three times as many function evaluations. Two well-known existing modifications for stochastic responses, the n + 3-rule and the next-to-worst rule, were found to be inferior to the new modification RS + S9.

Nelder-Mead simplex modifications for simulation optimization

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Modifications of the Nelder-Mead simplex method for stochastic simulation response optimization

Nelder-Mead simplex modifications for simulation optimization