A computational model of liver iron metabolism

Iron is essential for all known life due to its redox properties; however, these same properties can also lead to its toxicity in overload through the production of reactive oxygen species. Robust systemic and cellular control are required to maintain safe levels of iron, and the liver seems to be where this regulation is mainly located. Iron misregulation is implicated in many diseases, and as our understanding of iron metabolism improves, the list of iron-related disorders grows. Recent developments have resulted in greater knowledge of the fate of iron in the body and have led to a detailed map of its metabolism; however, a quantitative understanding at the systems level of how its components interact to produce tight regulation remains elusive. A mechanistic computational model of human liver iron metabolism, which includes the core regulatory components, is presented here. It was constructed based on known mechanisms of regulation and on their kinetic properties, obtained from several publications. The model was then quantitatively validated by comparing its results with previously published physiological data, and it is able to reproduce multiple experimental findings. A time course simulation following an oral dose of iron was compared to a clinical time course study and the simulation was found to recreate the dynamics and time scale of the systems response to iron challenge. A disease state simulation of haemochromatosis was created by altering a single reaction parameter that mimics a human haemochromatosis gene (HFE) mutation. The simulation provides a quantitative understanding of the liver iron overload that arises in this disease. This model supports and supplements understanding of the role of the liver as an iron sensor and provides a framework for further modelling, including simulations to identify valuable drug targets and design of experiments to improve further our knowledge of this system

The development of grade one teachers’ mathematics and pedagogical content knowledge through participation in a collaborative intervention

The “South African education system is grossly inefficient, severely underperforming and egregiously unfair” (Spaull, 2013, p.3). In particular, grave concerns with learner performance in mathematics in South Africa are well documented (e.g., Taylor, 2008; Spaull, 2013; Venkat & Spaull, 2015). There are various explanations for the poor state of learner performance in mathematics in South Africa. Two of the explanations that relate closely to my research interest are teachers’ insufficient mathematics content and pedagogical knowledge, and inappropriate professional development. This study aims to ascertain how a collaborative intervention can develop teachers’ mathematics and pedagogical content knowledge as they focus on developing learners’ foundational number sense. Cultural Historical Activity Theory, together with Mathematics Knowledge for Teaching (Ball et al., 2008) and the Knowledge Quartet (Rowlands & Turner, 2007) frameworks, provide the explanatory and analytic tools for the research. The research is a qualitative case study underpinned by an interpretivist orientation. The study was conducted at a township public primary school in the Northern Cape. Three Grade One teachers participated in the research. Data was collected through interviews, classroom observations, and videos of collaborative lesson planning and reflection sessions. A key finding emerging from this research is that the teachers had the necessary mathematics content knowledge to teach Grade One mathematics. Despite this and in contrast to it, they lacked adequate pedagogical content knowledge required to develop learners’ number sense. To develop their pedagogical content knowledge, they required the intervention of a ‘more knowledgable other’ (Vygotsky, 2008). Several contradictions and tensions emerged from the research. For example, the teachers expressed that the opportunity to work collaboratively was beneficial, but it was evident that they were familiar with and accomplished in planning and working together. The contradictions emerging from this research provide an opportunity and basis for expansive learning for future collaborative teacher endeavours.Thesis (MEd) -- Faculty of Education, Education, 202

An Evaluation of Methods for Inferring Boolean Networks from Time-Series Data

Regulatory networks play a central role in cellular behavior and decision making. Learning these regulatory networks is a major task in biology, and devising computational methods and mathematical models for this task is a major endeavor in bioinformatics. Boolean networks have been used extensively for modeling regulatory networks. In this model, the state of each gene can be either ‘on’ or ‘off’ and that next-state of a gene is updated, synchronously or asynchronously, according to a Boolean rule that is applied to the current-state of the entire system. Inferring a Boolean network from a set of experimental data entails two main steps: first, the experimental time-series data are discretized into Boolean trajectories, and then, a Boolean network is learned from these Boolean trajectories. In this paper, we consider three methods for data discretization, including a new one we propose, and three methods for learning Boolean networks, and study the performance of all possible nine combinations on four regulatory systems of varying dynamics complexities. We find that employing the right combination of methods for data discretization and network learning results in Boolean networks that capture the dynamics well and provide predictive power. Our findings are in contrast to a recent survey that placed Boolean networks on the low end of the ‘‘faithfulness to biological reality’’ and ‘‘ability to model dynamics’’ spectra. Further, contrary to the common argument in favor of Boolean networks, we find that a relatively large number of time points in the timeseries data is required to learn good Boolean networks for certain data sets. Last but not least, while methods have been proposed for inferring Boolean networks, as discussed above, missing still are publicly available implementations thereof. Here, we make our implementation of the methods available publicly in open source at http://bioinfo.cs.rice.edu/