Biology subjects

Murray, R.

Publications and source records attributed to Murray, R..

4 recordsLinked to original sources

bioRxiv2018

The Maudsley Environmental Risk Score for Psychosis

Risk prediction algorithms have long been used in health research and practice (e.g., in prediction of cardiovascular disease, diabetes, etc.) However, similar tools have not been developed for mental health problems, despite extensive research on risk factors. For example, for psychotic disorders, attempts to sum environmental risk are rare, usually unsystematic and dictated by available data. In light of this, we sought to develop a valid, easy to use measure of the total environmental risk for psychotic disorders, which can be used in research and clinical practice.\n\nWe first reviewed the literature to identify well-replicated and validated environmental risk factors for psychosis and, then, used the largest available meta-analyses to derive current best estimates of risk. We devised a method of scoring individuals based on the level of exposure to each risk factor, using odds ratios from the meta-analyses, to produce an Environmental Risk Score (ERS).\n\nSix risk factors (ethnic minority status, urbanicity, high paternal age, obstetric complications, cannabis use, and childhood adversity) were used to generate the ERS. A distribution for different levels of risk based on permuted data showed that most of population would be at low/moderate risk with a small minority at increased environmental risk for psychosis.\n\nThis is the first systematic approach to develop an aggregate measure of environmental risk for psychoses. This can be used as a continuous measure of liability to disease or transformed to a relative risk. Its predictive ability will improve with the collection of additional, population specific data.

epidemiology↗

bioRxiv2018

Context Dependence of Biological Circuits

It has been an ongoing scientific debate whether biological parameters are conserved across experimental setups with different media, pH values, and other experimental conditions. Our work explores this question using Bayesian probability as a rigorous framework to assess the biological context of parameters in a model of the cell growth controller in You et al. When this growth controller is uninduced, the E. coli cell population grows to carrying capacity; however, when the circuit is induced, the cell population growth is regulated to remain well below carrying capacity. This growth control controller regulates the E. coli cell population by cell-cell communication using the signaling molecule AHL and by cell death using the bacterial toxin CcdB.\n\nTo evaluate the context dependence of parameters such as the cell growth rate, the carrying capacity, the AHL degradation rate, the leakiness of AHL, the leakiness of toxin CcdB, and the IPTG induction factor, we collect experimental data from the growth control circuit in two different media, at two different pH values, and with several induction levels. We define a set of possible context-dependencies that describe how these parameters may differ with the experimental conditions and we develop mathematical models of the growth controller across the different experimental contexts. We then determine whether these parameters are shared across experimental contexts or whether they are context-dependent. For each of these possible context-dependencies, we use Bayesian inference to assess its plausibility and to estimate the growth controllers parameters assuming this context-dependency. Ultimately, we find that there is significant experimental context-dependence in this circuit. Moreover, we also find that the estimated parameter values are sensitive to our assumption of a context relationship.

synthetic biology↗

bioRxiv2017

Hard Limits And Performance Tradeoffs In A Class Of Sequestration Feedback Systems

Feedback regulation is pervasive in biology at both the organismal and cellular level. In this article, we explore the properties of a particular biomolecular feedback mechanism implemented using the sequestration binding of two molecules. Our work develops an analytic framework for understanding the hard limits, performance tradeoffs, and architectural properties of this simple model of biological feedback control. Using tools from control theory, we show that there are simple parametric relationships that determine both the stability and the performance of these systems in terms of speed, robustness, steady-state error, and leakiness. These findings yield a holistic understanding of the behavior of sequestration feedback and contribute to a more general theory of biological control systems.

systems biology↗

bioRxiv2016

A Bayesian approach to inferring chemical signal timing and amplitude in a temporal logic gate using the cell population distributional response

1. Introduction 1. Introduction 2. The temporal logic... 3. Bayesian event detection... 4. Applying the Bayesian... 5. Conclusion References Stochastic gene expression poses an important challenge for engineering robust behaviors in a heterogeneous cell population [1]. Cells address this challenge using the distribution of cellular responses during some gene regulation and differentiation processes [2]. Similarly, the temporal logic gate design in Hsiao et al. [3] considers the distribution of responses across a cell population. The design employs integrases Bxb1 and TP901-1 [4] to engineer an E. coli strain with four DNA states that record the temporal order of two chemical signals. Hsiao et al. [3] also use the heter ...

synthetic biology↗