Stochastic modeling of systems mapping in pharmacogenomics

Functional physiological phenotyping with functional mapping: A general framework to bridge the phenotype-genotype gap in plant physiology

The recent years have witnessed the emergence of high-throughput phenotyping techniques. In particular, these techniques can characterize a comprehensive landscape of physiological traits of plants responding to dynamic changes in the environment. These innovations, along with the next-generation genomic technologies, have brought plant science into the big-data era. However, a general framework that links multifaceted physiological traits to DNA variants is still lacking. Here, we developed a general framework that integrates functional physiological phenotyping (FPP) with functional mapping (FM). This integration, implemented with high-dimensional statistical reasoning, can aid in our understanding of how genotype is translated toward phenotype. As a demonstration of method, we implemented the transpiration and soil-plant-atmosphere measurements of a tomato introgression line population into the FPP-FM framework, facilitating the identification of quantitative trait loci (QTLs) that mediate the spatiotemporal change of transpiration rate and the test of how these QTLs control, through their interaction networks, phenotypic plasticity under drought stress.

Funmap2: an R package for QTL mapping using longitudinal phenotypes

Quantitative trait locus (QTL) mapping has been used as a powerful tool for inferring the complexity of the genetic architecture that underlies phenotypic traits. This approach has shown its unique power to map the developmental genetic architecture of complex traits by implementing longitudinal data analysis. Here, we introduce the R package Funmap2 based on the functional mapping framework, which integrates prior biological knowledge into the statistical model. Specifically, the functional mapping framework is engineered to include longitudinal curves that describe the genetic effects and the covariance matrix of the trait of interest. Funmap2 chooses the type of longitudinal curve and covariance matrix automatically using information criteria. Funmap2 is available for download at https://github.com/wzhy2000/Funmap2.

Mapping complex traits as a dynamic system

Despite increasing emphasis on the genetic study of quantitative traits, we are still far from being able to chart a clear picture of their genetic architecture, given an inherent complexity involved in trait formation. A competing theory for studying such complex traits has emerged by viewing their phenotypic formation as a "system" in which a high-dimensional group of interconnected components act and interact across different levels of biological organization from molecules through cells to whole organisms. This system is initiated by a machinery of DNA sequences that regulate a cascade of biochemical pathways to synthesize endophenotypes and further assemble these endophenotypes toward the end-point phenotype in virtue of various developmental changes. This review focuses on a conceptual framework for genetic mapping of complex traits by which to delineate the underlying components, interactions and mechanisms that govern the system according to biological principles and understand how these components function synergistically under the control of quantitative trait loci (QTLs) to comprise a unified whole. This framework is built by a system of differential equations that quantifies how alterations of different components lead to the global change of trait development and function, and provides a quantitative and testable platform for assessing the multiscale interplay between QTLs and development. The method will enable geneticists to shed light on the genetic complexity of any biological system and predict, alter or engineer its physiological and pathological states.

Parallel solutions for voxel-based simulations of reaction-diffusion systems

There is an increasing awareness of the pivotal role of noise in biochemical processes and of the effect of molecular crowding on the dynamics of biochemical systems. This necessity has given rise to a strong need for suitable and sophisticated algorithms for the simulation of biological phenomena taking into account both spatial effects and noise. However, the high computational effort characterizing simulation approaches, coupled with the necessity to simulate the models several times to achieve statistically relevant information on the model behaviours, makes such kind of algorithms very time-consuming for studying real systems. So far, different parallelization approaches have been deployed to reduce the computational time required to simulate the temporal dynamics of biochemical systems using stochastic algorithms. In this work we discuss these aspects for the spatial TAU-leaping in crowded compartments (STAUCC) simulator, a voxel-based method for the stochastic simulation of reaction-diffusion processes which relies on the Sτ-DPP algorithm. In particular we present how the characteristics of the algorithm can be exploited for an effective parallelization on the present heterogeneous HPC architectures.