Person:

Franks, Alexander M.

Genetic algorithms (GAs) have been used to find efficient solutions to numerous fundamental and applied problems. While GAs are a robust and flexible approach to solve complex problems, there are some situations under which they perform poorly. Here, we introduce a genetic algorithm approach that is able to solve complex tasks plagued by so-called ''golf-course''-like fitness landscapes. Our approach, which we denote variable environment genetic algorithms (VEGAs), is able to find highly efficient solutions by inducing environmental changes that require more complex solutions and thus creating an evolutionary drive. Using the density classification task, a paradigmatic computer science problem, as a case study, we show that more complex rules that preserve information about the solution to simpler tasks can adapt to more challenging environments. Interestingly, we find that conservative strategies, which have a bias toward the current state, evolve naturally as a highly efficient solution to the density classification task under noisy conditions.

One of the central challenges in systems biology research is disentangling relevant and irrelevant sources of variation. While the relevant quantities are always context dependent, an important distinction can be drawn between variability due to biological processes and variability due measurement error. Biological variability includes variability between mRNA or protein abundances within a well defined condition, variability of these abundances across conditions (physiological variability), and between species or between subject variability. Technical variability includes measurement error, technological bias, and variability due to missing data. In this dissertation, we explore statistical challenges associated with disentangling sources of variability, both biological and technical, in the analysis of high-throughput biological data. In the first chapter, we present a careful meta-analysis of 27 yeast data sets supported by a multilevel model to separate biological variability from structured technical variability. In the second chapter, we suggest a simple and general approach for deconvolving the contributions of orthogonal sources of biological variability, both between and within molecules, across multiple physiological conditions. The results discussed in these two chapters elucidate the relative importance of transcriptional and post-transcriptional regulation of protein levels. Finally, in the third chapter we introduce a novel approach for modeling non-ignorable missing data. We illustrate the utility of this methodology on missing data in mRNA and protein measurements.

Cells respond to their environment by modulating protein levels through mRNA transcription and post-transcriptional control. Modest observed correlations between global steady-state mRNA and protein measurements have been interpreted as evidence that mRNA levels determine roughly 40% of the variation in protein levels, indicating dominant post-transcriptional effects. However, the techniques underlying these conclusions, such as correlation and regression, yield biased results when data are noisy, missing systematically, and collinear---properties of mRNA and protein measurements---which motivated us to revisit this subject. Noise-robust analyses of 24 studies of budding yeast reveal that mRNA levels explain more than 85% of the variation in steady-state protein levels. Protein levels are not proportional to mRNA levels, but rise much more rapidly. Regulation of translation suffices to explain this nonlinear effect, revealing post-transcriptional amplification of, rather than competition with, transcriptional signals. These results substantially revise widely credited models of protein-level regulation, and introduce multiple noise-aware approaches essential for proper analysis of many biological phenomena.

Heat causes protein misfolding and aggregation, and in eukaryotic cells triggers aggregation of proteins and RNA into stress granules. We have carried out extensive proteomic studies to quantify heat-triggered aggregation and subsequent disaggregation in budding yeast, identifying more than 170 endogenous proteins aggregating within minutes of heat shock in multiple subcellular compartments. We demonstrate that these aggregated proteins are not misfolded and destined for degradation. Stable-isotope labeling reveals that even severely aggregated endogenous proteins are disaggregated without degradation during recovery from shock, contrasting with the rapid degradation observed for exogenous thermolabile proteins. Although aggregation likely inactivates many cellular proteins, in the case of a heterotrimeric aminoacyl-tRNA synthetase complex, the aggregated proteins remain active with unaltered fidelity. We propose that most heat-induced aggregation of mature proteins reflects the operation of an adaptive, autoregulatory process of functionally significant aggregate assembly and disassembly that aids cellular adaptation to thermal stress.