Reverse engineering of drug induced DNA damage response signalling pathway reveals dual outcomes of ATM kinase inhibition

The DNA Damage Response (DDR) pathway represents a signalling mechanism that is activated in eukaryotic cells following DNA damage and comprises of proteins involved in DNA damage detection, DNA repair, cell cycle arrest and apoptosis. This pathway consists of an intricate network of signalling interactions driving the cellular ability to recognise DNA damage and recruit specialised proteins to take decisions between DNA repair or apoptosis. ATM and ATR are central components of the DDR pathway. The activities of these kinases are vital in DNA damage induced phosphorylational induction of DDR substrates. Here, firstly we have experimentally determined DDR signalling network surrounding the ATM/ATR pathway induced following double stranded DNA damage by monitoring and quantifying time dependent inductions of their phosphorylated forms and their key substrates. We next involved an automated inference of unsupervised predictive models of time series data to generate in silico (molecular) interaction maps. We characterized the complex signalling network through system analysis and gradual utilisation of small time series measurements of key substrates through a novel network inference algorithm. Furthermore, we demonstrate an application of an assumption-free reverse engineering of the intricate signalling network of the activated ATM/ATR pathway. We next studied the consequences of such drug induced inductions as well as of time dependent ATM kinase inhibition on cell survival through further biological experiments. Intermediate and temporal modelling outcomes revealed the distinct signaling profile associated with ATM kinase activity and inhibition and explained the underlying signalling mechanism for dual ATM functionality in cytotoxic and cytoprotective pathways

Mathematical model of a telomerase transcriptional regulatory network developed by cell-based screening: analysis of inhibitor effects and telomerase expression mechanisms

Cancer cells depend on transcription of telomerase reverse transcriptase (TERT). Many transcription factors affect TERT, though regulation occurs in context of a broader network. Network effects on telomerase regulation have not been investigated, though deeper understanding of TERT transcription requires a systems view. However, control over individual interactions in complex networks is not easily achievable. Mathematical modelling provides an attractive approach for analysis of complex systems and some models may prove useful in systems pharmacology approaches to drug discovery. In this report, we used transfection screening to test interactions among 14 TERT regulatory transcription factors and their respective promoters in ovarian cancer cells. The results were used to generate a network model of TERT transcription and to implement a dynamic Boolean model whose steady states were analysed. Modelled effects of signal transduction inhibitors successfully predicted TERT repression by Src-family inhibitor SU6656 and lack of repression by ERK inhibitor FR180204, results confirmed by RT-QPCR analysis of endogenous TERT expression in treated cells. Modelled effects of GSK3 inhibitor 6-bromoindirubin-3′-oxime (BIO) predicted unstable TERT repression dependent on noise and expression of JUN, corresponding with observations from a previous study. MYC expression is critical in TERT activation in the model, consistent with its well known function in endogenous TERT regulation. Loss of MYC caused complete TERT suppression in our model, substantially rescued only by co-suppression of AR. Interestingly expression was easily rescued under modelled Ets-factor gain of function, as occurs in TERT promoter mutation. RNAi targeting AR, JUN, MXD1, SP3, or TP53, showed that AR suppression does rescue endogenous TERT expression following MYC knockdown in these cells and SP3 or TP53 siRNA also cause partial recovery. The model therefore successfully predicted several aspects of TERT regulation including previously unknown mechanisms. An extrapolation suggests that a dominant stimulatory system may programme TERT for transcriptional stability

Cardiac cell modelling: Observations from the heart of the cardiac physiome project

In this manuscript we review the state of cardiac cell modelling in the context of international initiatives such as the IUPS Physiome and Virtual Physiological Human Projects, which aim to integrate computational models across scales and physics. In particular we focus on the relationship between experimental data and model parameterisation across a range of model types and cellular physiological systems. Finally, in the context of parameter identification and model reuse within the Cardiac Physiome, we suggest some future priority areas for this field

The crosstalk between the ERK and the cAMP signalling pathways in PC12 Cells

The extracellular-regulated kinase (ERK) signalling pathway is involved in the control of different biological processes such as survival, proliferation and differentiation. In PC12 cells, the ERK signalling pathway integrates different external stimuli: epidermal growth factor (EGF) stimulates the ERK pathway transiently and induces cell proliferation, whereas nerve growth factor (NGF) induces sustained activation of ERK and promotes cell differentiation into sympathetic-like neurons. The second messenger 3’,5’-cyclic adenosine monophosphate (cAMP) controls a plethora of cellular events from metabolic to cellular signalling pathways. Over the years, lines of evidence have shown that cAMP is also involved in the regulation of cell growth and cell differentiation, suggesting a possible crosstalk between these two pathways. Selective phosphodiesterase (PDE) inhibitors were used to block the degradation of cAMP. These inhibitors increased the level of cAMP, but had different effects on the activation of ERK. Upon both NGF and EGF stimulations, cilostamide had the strongest effect and doubled the intensity of the phosphorylation of ERK, identifying PDE3 to control the level of cAMP relevant for the regulation of the ERK pathway. The treatment with cilostamide enhanced the differentiation of PC12 cells and the combination of both cilostamide and rolipram (a PDE4 inhibitor) turned the proliferative effect of EGF into a differentiation effect. The route for cAMP in the regulation of the ERK pathway was decomposed by using the cAMP analogues 8-pCPT-2’-O-Me-cAMP and 6-Bnz-cAMP. They specifically activate the Exchange protein activated by cyclic AMP (Epac) and the cAMP regulated protein kinase (PKA) respectively, which were hypothesised to be the effectors of cAMP in the regulation of ERK. The Epac agonist mimicked the effects of cilostamide on the activation of ERK, but failed to enhance cell differentiation. The PKA agonist reduced the phosphorylation ERK upon EGF. It was suggested that the activation of ERK in response to cAMP was mainly mediated through Epac rather than PKA, and that the activation of both PKA and Epac are required to induce cellular differentiation. To elucidate the differential regulation of the activation of ERK upon NGF and EGF stimulation and in response to cAMP, the activity of Ras and Rap1 were measured by affinity pulldown assays. Upon EGF the signal was transduced through Ras only, whereas upon NGF the signal was mediated through both Ras and Rap1. cAMP sensitised Rap1 that became activated upon EGF stimulation indicating that cAMP can switch on the Rap1/B-Raf pathway. This correlated with the increase in the phosphorylation of ERK in response to high levels of cAMP upon EGF stimulation. Then, the role of Raf-1 and C3G, a guanine exchange factor for Rap1, were investigated using small interfering RNA. The depletion of Raf-1 showed that Raf-1 is not essential for transducing the mitogen signal upon NGF stimulation and suggested that Ras mediates the signal through B-Raf upon EGF stimulation to compensate for the loss of Raf-1. The depletion of C3G also confirmed that the activation of ERK in response to cAMP is mediated through the Rap1/B-Raf pathway. Finally, the interaction between Raf-1 and AKAP79 was demonstrated for the first time suggesting the existence of a complex between Raf-1, AKAP and PKA and therefore a possible molecular mechanism for the inhibition of Raf-1 by cAMP through PKA. The data presented in this thesis demonstrates that cAMP participates to finely tune the regulation of the ERK signalling pathway and can be use as a tool to elucidate the network comprising the ERK cascade

Mathematical Modelling of Cell-Fate Decision in Response to Death Receptor Engagement

Cytokines such as TNF and FASL can trigger death or survival depending on cell lines and cellular conditions. The mechanistic details of how a cell chooses among these cell fates are still unclear. The understanding of these processes is important since they are altered in many diseases, including cancer and AIDS. Using a discrete modelling formalism, we present a mathematical model of cell fate decision recapitulating and integrating the most consistent facts extracted from the literature. This model provides a generic high-level view of the interplays between NFκB pro-survival pathway, RIP1-dependent necrosis, and the apoptosis pathway in response to death receptor-mediated signals. Wild type simulations demonstrate robust segregation of cellular responses to receptor engagement. Model simulations recapitulate documented phenotypes of protein knockdowns and enable the prediction of the effects of novel knockdowns. In silico experiments simulate the outcomes following ligand removal at different stages, and suggest experimental approaches to further validate and specialise the model for particular cell types. We also propose a reduced conceptual model implementing the logic of the decision process. This analysis gives specific predictions regarding cross-talks between the three pathways, as well as the transient role of RIP1 protein in necrosis, and confirms the phenotypes of novel perturbations. Our wild type and mutant simulations provide novel insights to restore apoptosis in defective cells. The model analysis expands our understanding of how cell fate decision is made. Moreover, our current model can be used to assess contradictory or controversial data from the literature. Ultimately, it constitutes a valuable reasoning tool to delineate novel experiments

The influence of dopamine on prediction, action and learning

In this thesis I explore functions of the neuromodulator dopamine in the context of autonomous learning and behaviour. I first investigate dopaminergic influence within a simulated agent-based model, demonstrating how modulation of synaptic plasticity can enable reward-mediated learning that is both adaptive and self-limiting. I describe how this mechanism is driven by the dynamics of agentenvironment interaction and consequently suggest roles for both complex spontaneous neuronal activity and specific neuroanatomy in the expression of early, exploratory behaviour. I then show how the observed response of dopamine neurons in the mammalian basal ganglia may also be modelled by similar processes involving dopaminergic neuromodulation and cortical spike-pattern representation within an architecture of counteracting excitatory and inhibitory neural pathways, reflecting gross mammalian neuroanatomy. Significantly, I demonstrate how combined modulation of synaptic plasticity and neuronal excitability enables specific (timely) spike-patterns to be recognised and selectively responded to by efferent neural populations, therefore providing a novel spike-timing based implementation of the hypothetical ‘serial-compound’ representation suggested by temporal difference learning. I subsequently discuss more recent work, focused upon modelling those complex spike-patterns observed in cortex. Here, I describe neural features likely to contribute to the expression of such activity and subsequently present novel simulation software allowing for interactive exploration of these factors, in a more comprehensive neural model that implements both dynamical synapses and dopaminergic neuromodulation. I conclude by describing how the work presented ultimately suggests an integrated theory of autonomous learning, in which direct coupling of agent and environment supports a predictive coding mechanism, bootstrapped in early development by a more fundamental process of trial-and-error learning

Identification of the YfgF MASE1 domain as a modulator of bacterial responses to aspartate

Complex 3'-5'-cyclic diguanylic acid (c-di-GMP) responsive regulatory networks that are modulated by the action of multiple diguanylate cyclases (DGC; GGDEF domain proteins) and phosphodiesterases (PDE; EAL domain proteins) have evolved in many bacteria. YfgF proteins possess a membrane-anchoring domain (MASE1), a catalytically inactive GGDEF domain and a catalytically active EAL domain. Here, sustained expression of the Salmonella enterica spp. Enterica ser. Enteritidis YfgF protein is shown to mediate inhibition of the formation of the aspartate chemotactic ring on motility agar under aerobic conditions. This phenomenon was c-di-GMP-independent because it occurred in a Salmonella strain that lacked the ability to synthesize c-di-GMP and also when PDE activity was abolished by site-directed mutagenesis of the EAL domain. YfgF-mediated inhibition of aspartate chemotactic ring formation was impaired in the altered redox environment generated by exogenous p-benzoquinone. This ability of YfgF to inhibit the response to aspartate required a motif, (213)Lys-Lys-Glu(215), in the predicted cytoplasmic loop between trans-membrane regions 5 and 6 of the MASE1 domain. Thus, for the first time the function of a MASE1 domain as a redox-responsive regulator of bacterial responses to aspartate has been shown

Investigating biocomplexity through the agent-based paradigm.

Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines--or agents--to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex