Modelling Gene Regulatory Networks

This thesis presents the results of mathematical modeling of both individual genes and small networks of genes. The regulation of gene activity is essential for the proper functioning of cells, which employ a variety of molecular mechanisms to control gene expression. Despite this, there is considerable variation in the precise number and timing of protein molecules that are produced. This is because gene expression is fundamentally a noisy process, subject to a number of sources of randomness, including uctuations in metabolite levels, the environment and ampli ed by the very low number of molecules involved. I have developed a probabilistic model of the burst size distribution (the number of proteins produced by the binding of one promoter) of a single gene. Recent experimental data provides excellent agreement with the model, but also reveals limitations of currently available data in determining the origin of variations in expression. A second strand of my work has addressed the dynamics of networks of genes. A network motif is a sub-graph that occurs more often in the network than would be expected by chance. The recurrent presence of certain motifs has been linked to systematic di erences in the functional properties of networks. I have developed models of the possible dynamical behaviour, in particular for the bi-fan motif, a small sub-network with four genes. This motif has been identi ed as the most prevalent in the regulatory networks of both the bacterium Escherichia coli and Saccharaomyces cerevisiae. The results of this work show that the microscopic details of the interactions are of paramount importance, with few inherent constraints on the network dynamics from consideration of network structure alone. This result is relevant to all attempts to model gene networks without su ciently detailed knowledge of the mechanisms of interaction

Network motifs: structure does not determine function

BACKGROUND: A number of publications have recently examined the occurrence and properties of the feed-forward motif in a variety of networks, including those that are of interest in genome biology, such as gene networks. The present work looks in some detail at the dynamics of the bi-fan motif, using systems of ordinary differential equations to model the populations of transcription factors, mRNA and protein, with the aim of extending our understanding of what appear to be important building blocks of gene network structure. RESULTS: We develop an ordinary differential equation model of the bi-fan motif and analyse variants of the motif corresponding to its behaviour under various conditions. In particular, we examine the effects of different steady and pulsed inputs to five variants of the bifan motif, based on evidence in the literature of bifan motifs found in Saccharomyces cerevisiae (commonly known as baker's yeast). Using this model, we characterize the dynamical behaviour of the bi-fan motif for a wide range of biologically plausible parameters and configurations. We find that there is no characteristic behaviour for the motif, and with the correct choice of parameters and of internal structure, very different, indeed even opposite behaviours may be obtained. CONCLUSION: Even with this relatively simple model, the bi-fan motif can exhibit a wide range of dynamical responses. This suggests that it is difficult to gain significant insights into biological function simply by considering the connection architecture of a gene network, or its decomposition into simple structural motifs. It is necessary to supplement such structural information by kinetic parameters, or dynamic time series experimental data, both of which are currently difficult to obtain

Nonidentifiability of the Source of Intrinsic Noise in Gene Expression from Single-Burst Data

Over the last few years, experimental data on the fluctuations in gene activity between individual cells and within the same cell over time have confirmed that gene expression is a “noisy” process. This variation is in part due to the small number of molecules taking part in some of the key reactions that are involved in gene expression. One of the consequences of this is that protein production often occurs in bursts, each due to a single promoter or transcription factor binding event. Recently, the distribution of the number of proteins produced in such bursts has been experimentally measured, offering a unique opportunity to study the relative importance of different sources of noise in gene expression. Here, we provide a derivation of the theoretical probability distribution of these bursts for a wide variety of different models of gene expression. We show that there is a good fit between our theoretical distribution and that obtained from two different published experimental datasets. We then prove that, irrespective of the details of the model, the burst size distribution is always geometric and hence determined by a single parameter. Many different combinations of the biochemical rates for the constituent reactions of both transcription and translation will therefore lead to the same experimentally observed burst size distribution. It is thus impossible to identify different sources of fluctuations purely from protein burst size data or to use such data to estimate all of the model parameters. We explore methods of inferring these values when additional types of experimental data are available

Determinants of Unlawful File Sharing: A Scoping Review

We employ a scoping review methodology to consider and assess the existing evidence on the determinants of unlawful file sharing (UFS) transparently and systematically. Based on the evidence, we build a simple conceptual framework to model the psychological decision to engage in UFS, purchase legally or do nothing. We identify social, moral, experiential, technical, legal and financial utility sources of the decision to purchase or to file share. They interact in complex ways. We consider the strength of evidence within these areas and note patterns of results. There is good evidence for influences on UFS within each of the identified determinants, particularly for self-reported measures, with more behavioral research needed. There are also indications that the reasons for UFS differ across media; more studies exploring media other than music are required

Abstracts from the NIHR INVOLVE Conference 2017

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Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Distinct genetic architectures for syndromic and nonsyndromic congenital heart defects identified by exome sequencing.

Congenital heart defects (CHDs) have a neonatal incidence of 0.8-1% (refs. 1,2). Despite abundant examples of monogenic CHD in humans and mice, CHD has a low absolute sibling recurrence risk (∼2.7%), suggesting a considerable role for de novo mutations (DNMs) and/or incomplete penetrance. De novo protein-truncating variants (PTVs) have been shown to be enriched among the 10% of 'syndromic' patients with extra-cardiac manifestations. We exome sequenced 1,891 probands, including both syndromic CHD (S-CHD, n = 610) and nonsyndromic CHD (NS-CHD, n = 1,281). In S-CHD, we confirmed a significant enrichment of de novo PTVs but not inherited PTVs in known CHD-associated genes, consistent with recent findings. Conversely, in NS-CHD we observed significant enrichment of PTVs inherited from unaffected parents in CHD-associated genes. We identified three genome-wide significant S-CHD disorders caused by DNMs in CHD4, CDK13 and PRKD1. Our study finds evidence for distinct genetic architectures underlying the low sibling recurrence risk in S-CHD and NS-CHD