The Neurodegenerative Disease Knowledge Portal: Propelling Discovery Through the Sharing of Neurodegenerative Disease Genomic Resources.

Although large-scale genetic association studies have proven opportunistic for the delineation of neurodegenerative disease processes, we still lack a full understanding of the pathological mechanisms of these diseases, resulting in few appropriate treatment options and diagnostic challenges. To mitigate these gaps, the Neurodegenerative Disease Knowledge Portal (NDKP) was created as an open-science initiative with the aim to aggregate, enable analysis, and display all available genomic datasets of neurodegenerative disease, while protecting the integrity and confidentiality of the underlying datasets. The portal contains 218 genomic datasets, including genotyping and sequencing studies, of individuals across ten different phenotypic groups, including neurological conditions such as Alzheimer's disease, amyotrophic lateral sclerosis, Lewy body dementia, and Parkinson's disease. In addition to securely hosting large genomic datasets, the NDKP provides accessible workflows and tools to effectively utilize the datasets and assist in the facilitation of customized genomic analyses. Here, we summarize the genomic datasets currently included within the portal, the bioinformatics processing of the datasets, and the variety of phenotypes captured. We also present example use-cases of the various user interfaces and integrated analytic tools to demonstrate their extensive utility in enabling the extraction of high-quality results at the source, for both genomics experts and those in other disciplines. Overall, the NDKP promotes open-science and collaboration, maximizing the potential for discovery from the large-scale datasets researchers and consortia are expending immense resources to produce and resulting in reproducible conclusions to improve diagnostic and therapeutic care for neurodegenerative disease patients.

Assessing the genetic contribution of cumulative behavioral factors associated with longitudinal type 2 diabetes risk highlights adiposity and the brain-metabolic axis.

While genetic factors, behavior, and environmental exposures form a complex web of interrelated associations in type 2 diabetes (T2D), their interaction is poorly understood. Here, using data from ~500K participants of the UK Biobank, we identify the genetic determinants of a "polyexposure risk score" (PXS) a new risk factor that consists of an accumulation of 25 associated individual-level behaviors and environmental risk factors that predict longitudinal T2D incidence. PXS-T2D had a non-zero heritability (h2 = 0.18) extensive shared genetic architecture with established clinical and biological determinants of T2D, most prominently with body mass index (genetic correlation [rg] = 0.57) and Homeostatic Model Assessment for Insulin Resistance (rg = 0.51). Genetic loci associated with PXS-T2D were enriched for expression in the brain. Biobank scale data with genetic information illuminates how complex and cumulative exposures and behaviors as a whole impact T2D risk but whose biology have been elusive in genome-wide studies of T2D.

The Type 2 Diabetes Knowledge Portal: An open access genetic resource dedicated to type 2 diabetes and related traits.

Associations between human genetic variation and clinical phenotypes have become a foundation of biomedical research. Most repositories of these data seek to be disease-agnostic and therefore lack disease-focused views. The Type 2 Diabetes Knowledge Portal (T2DKP) is a public resource of genetic datasets and genomic annotations dedicated to type 2 diabetes (T2D) and related traits. Here, we seek to make the T2DKP more accessible to prospective users and more useful to existing users. First, we evaluate the T2DKP's comprehensiveness by comparing its datasets with those of other repositories. Second, we describe how researchers unfamiliar with human genetic data can begin using and correctly interpreting them via the T2DKP. Third, we describe how existing users can extend their current workflows to use the full suite of tools offered by the T2DKP. We finally discuss the lessons offered by the T2DKP toward the goal of democratizing access to complex disease genetic results.

Coalescent models for developmental biology and the spatio-temporal dynamics of growing tissues.

Development is a process that needs to be tightly coordinated in both space and time. Cell tracking and lineage tracing have become important experimental techniques in developmental biology and allow us to map the fate of cells and their progeny. A generic feature of developing and homeostatic tissues that these analyses have revealed is that relatively few cells give rise to the bulk of the cells in a tissue; the lineages of most cells come to an end quickly. Computational and theoretical biologists/physicists have, in response, developed a range of modelling approaches, most notably agent-based modelling. These models seem to capture features observed in experiments, but can also become computationally expensive. Here, we develop complementary genealogical models of tissue development that trace the ancestry of cells in a tissue back to their most recent common ancestors. We show that with both bounded and unbounded growth simple, but universal scaling relationships allow us to connect coalescent theory with the fractal growth models extensively used in developmental biology. Using our genealogical perspective, it is possible to study bulk statistical properties of the processes that give rise to tissues of cells, without the need for large-scale simulations.

On a theory of stability for nonlinear stochastic chemical reaction networks.

We present elements of a stability theory for small, stochastic, nonlinear chemical reaction networks. Steady state probability distributions are computed with zero-information (ZI) closure, a closure algorithm that solves chemical master equations of small arbitrary nonlinear reactions. Stochastic models can be linearized around the steady state with ZI-closure, and the eigenvalues of the Jacobian matrix can be readily computed. Eigenvalues govern the relaxation of fluctuation autocorrelation functions at steady state. Autocorrelation functions reveal the time scales of phenomena underlying the dynamics of nonlinear reaction networks. In accord with the fluctuation-dissipation theorem, these functions are found to be congruent to response functions to small perturbations. Significant differences are observed in the stability of nonlinear reacting systems between deterministic and stochastic modeling formalisms.

Chemical master equation closure for computer-aided synthetic biology.

With inexpensive DNA synthesis technologies, we can now construct biological systems by quickly piecing together DNA sequences. Synthetic biology is the promising discipline that focuses on the construction of these new biological systems. Synthetic biology is an engineering discipline, and as such, it can benefit from mathematical modeling. This chapter focuses on mathematical models of biological systems. These models take the form of chemical reaction networks. The importance of stochasticity is discussed and methods to simulate stochastic reaction networks are reviewed. A closure scheme solution is also presented for the master equation of chemical reaction networks. The master equation is a complete model of randomly evolving molecular populations. Because of its ambitious character, the master equation remained unsolved for all but the simplest of molecular interaction networks for over 70 years. With the first complete solution of chemical master equations, a wide range of experimental observations of biomolecular interactions may be mathematically conceptualized. We anticipate that models based on the closure scheme described herein may assist in rationally designing synthetic biological systems.

Solution of Chemical Master Equations for Nonlinear Stochastic Reaction Networks.

Stochasticity in the dynamics of small reacting systems requires discrete-probabilistic models of reaction kinetics instead of traditional continuous-deterministic ones. The master probability equation is a complete model of randomly evolving molecular populations. Because of its ambitious character, the master equation remained unsolved for all but the simplest of molecular interaction networks. With the first solution of chemical master equations, a wide range of experimental observations of small-system interactions may be mathematically conceptualized.

A closure scheme for chemical master equations.

Probability reigns in biology, with random molecular events dictating the fate of individual organisms, and propelling populations of species through evolution. In principle, the master probability equation provides the most complete model of probabilistic behavior in biomolecular networks. In practice, master equations describing complex reaction networks have remained unsolved for over 70 years. This practical challenge is a reason why master equations, for all their potential, have not inspired biological discovery. Herein, we present a closure scheme that solves the master probability equation of networks of chemical or biochemical reactions. We cast the master equation in terms of ordinary differential equations that describe the time evolution of probability distribution moments. We postulate that a finite number of moments capture all of the necessary information, and compute the probability distribution and higher-order moments by maximizing the information entropy of the system. An accurate order closure is selected, and the dynamic evolution of molecular populations is simulated. Comparison with kinetic Monte Carlo simulations, which merely sample the probability distribution, demonstrates this closure scheme is accurate for several small reaction networks. The importance of this result notwithstanding, a most striking finding is that the steady state of stochastic reaction networks can now be readily computed in a single-step calculation, without the need to simulate the evolution of the probability distribution in time.

Stochastic model reduction using a modified Hill-type kinetic rate law.

In the present work, we address a major challenge facing the modeling of biochemical reaction networks: when using stochastic simulations, the computational load and number of unknown parameters may dramatically increase with system size and complexity. A proposed solution to this challenge is the reduction of models by utilizing nonlinear reaction rate laws in place of a complex multi-reaction mechanism. This type of model reduction in stochastic systems often fails when applied outside of the context in which it was initially conceived. We hypothesize that the use of nonlinear rate laws fails because a single reaction is inherently Poisson distributed and cannot match higher order statistics. In this study we explore the use of Hill-type rate laws as an approximation for gene regulation, specifically transcription repression. We matched output data for several simple gene networks to determine Hill-type parameters. We show that the models exhibit inaccuracies when placed into a simple feedback repression model. By adding an additional abstract reaction to the models we account for second-order statistics. This split Hill rate law matches higher order statistics and demonstrates that the new model is able to more accurately describe the mean protein output. Finally, the modified Hill model is shown to be modular and models retain accuracy when placed into a larger multi-gene network. The work as presented may be used in gene regulatory or cell-signaling networks, where multiple binding events can be captured by Hill kinetics. The added benefit of the proposed split-Hill kinetics is the improved accuracy in modeling stochastic effects. We demonstrate these benefits with a few specific reaction network examples.