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The study of genetically identical cells frequently reveals that substantial variation exists between the expression levels of molecules, such as mRNAs and proteins, with respect to time in individual cells and across a population. This is due to intrinsic noise arising from the random timing of biochemical reactions in the gene regulatory network. An additional source of noise in biological systems comes from interactions of unknown molecular components with the network, which are collectively termed extrinsic noise. Mathematical modelling has been used to understand the factors that contribute to the resulting stochasticity in the gene expression dynamics of cells. As many molecular species are present in small numbers, we require a stochastic description of these biochemical systems. Chapter 1 gives a general introduction into the sources of gene expression noise and non-genetic cell-to-cell variability. Throughout Chapters 2-5, we investigate cell-to-cell variability in a wide range of biological systems through the integration of experimental approaches and mathematical modelling.In Chapter 2, we derive a novel approximate method to obtain closed-form expressions for the means, variances, and power spectra of the molecular species within a chemical reaction network with intrinsic and slowly changing extrinsic noise. We do this by extending the conventional linear-noise approximation (LNA) to include systems where extrinsic noise manifests itself as fluctuations in the reaction rates. These extrinsic fluctuations are characterised by a longer timescale compared to the typically fast intrinsic reaction processes. We verify the accuracy of the theory by its application to different models of gene regulatory networks and comparing the analytical predictions to the more computationally costly results produced through stochastic simulations.In Chapter 3, we study the bacterial adaptive immune system CRISPR-Cas in a growing population of Escherichia coli. To survive i

Contains fulltext : 215118.pdf (Publisher’s version ) (Closed access), According to attachment theory, being securely attached to one's primary caregiver early in life should be related to personality adulthood. However, no studies to date have investigated this key premise using prospective data. To address this gap, we discuss evolutionary-based models of attachment and use them to examine how secure versus insecure children might score differently on Big 5 traits that underlie the meta-trait stability. We modeled data from Minnesota Longitudinal Study of Risk and Adaptation (N = 170), which has followed participants across 30 years. Participant's early attachment status was assessed in Ainsworth's Strange at 12 and 18 months and personality was assessed on Big 5 measures at age 32. Being securely attached early in childhood predicted three of the Big 5 traits known to tap the meta-trait stability. Specifically, participants rated as secure early in life scored higher on agreeableness and conscientiousness and lower on neuroticism in adulthood, whereas those rated as insecure scored lower on agreeableness and conscientiousness and higher on neuroticism. Exploratory mediation analyses revealed that neither adult attachment representations nor internalizing/externalizing symptoms mediated the association between early security and stability. The implications of these findings for understanding the origins of personality variation are discussed.

Modern biological research is accumulating an ever-increasing amount of information on genes and their functions. It is apparent that biological functions can very rarely be attributed to a single genes, but rather arise from complex interaction within networks that comprise many genes. A fundamentally important challenge in contemporary biology is to extract mechanistic understanding about the complex behavior of genetic networks from the available data. The interactions within a genetic network are often exceedingly complex and no-linear in nature, and thus are not open to intuitive understanding. This situation has given rise to a host of mathematical and computational approaches aimed at in-depth analysis of genetic network topologies and dynamics. In particular these approaches focus on system level proprieties of these networks, not directly derivable from their constituent components. To a large extent the power of these theoretical approaches rely on meaningful reduction in complexity by utilizing justified simplifications and abstractions. The underlying principle is that in order to comprehend a mechanism, it is not necessary to take into account all the available information about the mechanism. Given this, Computational models that follow this approach focus on incorporating core components that are essential in answering a specific biological question, while simplifying/omitting the less relevant processes. A fundamental question is this regard is what simplifying concept should be employed when developing a theoretical model of a genetic network. A successful approach to address this question is the notion of network motif analysis. This approach is based on the core idea that most genetic networks are not arbitrary nor unique, instead they can be categorized into common network dynamics and topologies that perform core functions. Analogous to components of an electric circuit (resistors, capacitor, etc.) these network motifs have distinct properties t

Systems Biology is an approach to biology and medicine that has the potential to lead to a better understanding of how biological properties emerge from the interaction of genes, proteins, molecules, cells and organisms. The approach aims at elucidating how these interactions govern biological function by employing experimental data, mathematical models and computational simulations. As Systems Biology is inherently multidisciplinary, education within this field meets numerous hurdles including departmental barriers, availability of all required expertise locally, appropriate teaching material and example curricula. As university education at the Bachelor’s level is traditionally built upon disciplinary degrees, we believe that the most effective way to implement education in Systems Biology would be at the Master’s level, as it offers a more flexible framework. Our team of experts and active performers of Systems Biology education suggest here (i) a definition of the skills that students should acquire within a Master’s programme in Systems Biology, (ii) a possible basic educational curriculum with flexibility to adjust to different application areas and local research strengths, (iii) a description of possible career paths for students who undergo such an education, (iv) conditions that should improve the recruitment of students to such programmes and (v) mechanisms for collaboration and excellence spreading among education professionals. With the growing interest of industry in applying Systems Biology approaches in their fields, a concerted action between academia and industry is needed to build this expertise. Here we present a reflection of the European situation and expertise, where most of the challenges we discuss are universal, anticipating that our suggestions will be useful internationally. We believe that one of the overriding goals of any Systems Biology education should be a student’s ability to phrase and communicate research questions in such a mann

Systems Biology is an approach to biology and medicine that has the potential to lead to a better understanding of how biological properties emerge from the interaction of genes, proteins, molecules, cells and organisms. The approach aims at elucidating how these interactions govern biological function by employing experimental data, mathematical models and computational simulations. As Systems Biology is inherently multidisciplinary, education within this field meets numerous hurdles including departmental barriers, availability of all required expertise locally, appropriate teaching material and example curricula. As university education at the Bachelor’s level is traditionally built upon disciplinary degrees, we believe that the most effective way to implement education in Systems Biology would be at the Master’s level, as it offers a more flexible framework. Our team of experts and active performers of Systems Biology education suggest here (i) a definition of the skills that students should acquire within a Master’s programme in Systems Biology, (ii) a possible basic educational curriculum with flexibility to adjust to different application areas and local research strengths, (iii) a description of possible career paths for students who undergo such an education, (iv) conditions that should improve the recruitment of students to such programmes and (v) mechanisms for collaboration and excellence spreading among education professionals. With the growing interest of industry in applying Systems Biology approaches in their fields, a concerted action between academia and industry is needed to build this expertise. Here we present a reflection of the European situation and expertise, where most of the challenges we discuss are universal, anticipating that our suggestions will be useful internationally. We believe that one of the overriding goals of any Systems Biology education should be a student’s ability to phrase and communicate research questions in such a mann