Elucidating the genotype-phenotype map by automatic enumeration and analysis of the phenotypic repertoire.

BackgroundThe gap between genotype and phenotype is filled by complex biochemical systems most of which are poorly understood. Because these systems are complex, it is widely appreciated that quantitative understanding can only be achieved with the aid of mathematical models. However, formulating models and measuring or estimating their numerous rate constants and binding constants is daunting. Here we present a strategy for automating difficult aspects of the process.MethodsThe strategy, based on a system design space methodology, is applied to a class of 16 designs for a synthetic gene oscillator that includes seven designs previously formulated on the basis of experimentally measured and estimated parameters.ResultsOur strategy provides four important innovations by automating: (1) enumeration of the repertoire of qualitatively distinct phenotypes for a system; (2) generation of parameter values for any particular phenotype; (3) simultaneous realization of parameter values for several phenotypes to aid visualization of transitions from one phenotype to another, in critical cases from functional to dysfunctional; and (4) identification of ensembles of phenotypes whose expression can be phased to achieve a specific sequence of functions for rationally engineering synthetic constructs. Our strategy, applied to the 16 designs, reproduced previous results and identified two additional designs capable of sustained oscillations that were previously missed.ConclusionsStarting with a system's relatively fixed aspects, its architectural features, our method enables automated analysis of nonlinear biochemical systems from a global perspective, without first specifying parameter values. The examples presented demonstrate the efficiency and power of this automated strategy

Improvements to Nucleon Matrix Elements within a θ\theta Vacuum from Lattice QCD

Using the gradient flow, we calculated the nucleon mixing angle $\alpha_{N}$ and the nucleon electric dipole moment (EDM) induced by the QCD $\theta$-term. To do so, we computed the topological charge, and the nucleon two-point and three-point correlation functions. The purpose of these proceedings is to explore how the topological charge density interacts with the nucleon interpolation operators. By understanding this relation, we can try to suppress noise contributions to the $\alpha_{N}$ and EDM signals by selecting specific regions where the signal dominates. Using gauge fields provided by PACS-CS at $N_{f}=2+1$, a first collection of ensembles were selected at a fixed lattice spacing $a=0.0907$ fm ($\beta=1.90$), fixed dimensions $32^{3}\times 64$ and varying $m_{\pi}\approx\lbrace 411,\,570,\,701\rbrace$ MeV. A second collection was selected at fixed $m_{\pi}\approx701$ MeV, fixed box size of $L\approx1.9$ fm and varying $a=\lbrace 0.1215,\,0.0980,\,0.0685\rbrace$ fm.Comment: 7 pages, 12 figures, presented at the 36th International Symposium on Lattice Field Theory (Lattice 2018

Robust Detection of Dynamic Community Structure in Networks

We describe techniques for the robust detection of community structure in some classes of time-dependent networks. Specifically, we consider the use of statistical null models for facilitating the principled identification of structural modules in semi-decomposable systems. Null models play an important role both in the optimization of quality functions such as modularity and in the subsequent assessment of the statistical validity of identified community structure. We examine the sensitivity of such methods to model parameters and show how comparisons to null models can help identify system scales. By considering a large number of optimizations, we quantify the variance of network diagnostics over optimizations (`optimization variance') and over randomizations of network structure (`randomization variance'). Because the modularity quality function typically has a large number of nearly-degenerate local optima for networks constructed using real data, we develop a method to construct representative partitions that uses a null model to correct for statistical noise in sets of partitions. To illustrate our results, we employ ensembles of time-dependent networks extracted from both nonlinear oscillators and empirical neuroscience data.Comment: 18 pages, 11 figure