In-silico investigation of the neonatal brain physiology using a systems biology approach: modelling birth asphyxia and neuroprotective strategies

Hypoxic ischaemic encephelopathy (HIE), often resulting from intrapartum hypoxic-ischemic injury, is a significant cause of death and morbidity before, during and after birth. In order to identify and monitor HIE, clinicians use non-invasive techniques including magnetic resonance spectroscopy (MRS) and near-infrared spectroscopy (NIRS). However, interpretation of these signals, particularly to determine the effectiveness of treatment and the severity of injury, is a challenging and difficult task. This thesis describes an attempt to use a systems biology approach to better understand the mechanisms behind HIE and its outcomes, using mathematical and computational techniques to analyse multimodal data, including broadband near-infrared spectroscopy (bNIRS). These models incorporate submodels of cerebral blood flow, oxygen transport and metabolism into a single cohesive model that attempts to simulate the observed measurements of tissue oxygenation and metabolism. The scope of this work is to both develop a set of computational tools that can be used to better understand existing systems biology models of the brain and to develop a new model which is able to incorporate the effects of therapeutic hypothermia, a common treatment for HIE, on the underlying physiology and its dynamics. The work begins by redeveloping the existing framework used for running and analysing systems biology models as used previously, before going on to develop a Bayesian framework which allows a better and more comprehensive interpretation of the results. This framework is then used to analyse three new models that incorporate the impact of therapeutic hypothermia on the piglet brain. The model determined to be most effective is then applied to clinical data from neonates that experience spontaneous desaturations in blood oxygen whilst undergoing hypothermic treatment. In all cases data from subjects with both mild and severe injuries are compared to determine if separate parameter spaces (and therefore physiological mechanisms) can be identified for each

Chapter Developing a Model to Simulate the Effect of Hypothermia on Cerebral Blood Flow and Metabolism

Hypoxic ischemic encephalopathy (HIE) is a significant cause of death and neurological disability in newborns. Therapeutic hypothermia at 33.5 °C is one of the most common treatments in HIE and generally improves outcome; however 45–55% of injuries still result in death or severe neurodevelopmental disability. We have developed a systems biology model of cerebral oxygen transport and metabolism to model the impact of hypothermia on the piglet brain (the neonatal preclinical animal model) tissue physiology. This computational model is an extension of the BrainSignals model of the adult brain. The model predicts that during hypothermia there is a 5.1% decrease in cerebral metabolism, 1.1% decrease in blood flow and 2.3% increase in cerebral tissue oxygenation saturation. The model can be used to simulate effects of hypothermia on the brain and to help interpret bedside recordings

Chapter Developing a Model to Simulate the Effect of Hypothermia on Cerebral Blood Flow and Metabolism

Hypoxic ischemic encephalopathy (HIE) is a significant cause of death and neurological disability in newborns. Therapeutic hypothermia at 33.5 °C is one of the most common treatments in HIE and generally improves outcome; however 45–55% of injuries still result in death or severe neurodevelopmental disability. We have developed a systems biology model of cerebral oxygen transport and metabolism to model the impact of hypothermia on the piglet brain (the neonatal preclinical animal model) tissue physiology. This computational model is an extension of the BrainSignals model of the adult brain. The model predicts that during hypothermia there is a 5.1% decrease in cerebral metabolism, 1.1% decrease in blood flow and 2.3% increase in cerebral tissue oxygenation saturation. The model can be used to simulate effects of hypothermia on the brain and to help interpret bedside recordings

A Bayesian framework for the analysis of systems biology models of the brain.

Systems biology models are used to understand complex biological and physiological systems. Interpretation of these models is an important part of developing this understanding. These models are often fit to experimental data in order to understand how the system has produced various phenomena or behaviour that are seen in the data. In this paper, we have outlined a framework that can be used to perform Bayesian analysis of complex systems biology models. In particular, we have focussed on analysing a systems biology of the brain using both simulated and measured data. By using a combination of sensitivity analysis and approximate Bayesian computation, we have shown that it is possible to obtain distributions of parameters that can better guard against misinterpretation of results, as compared to a maximum likelihood estimate based approach. This is done through analysis of simulated and experimental data. NIRS measurements were simulated using the same simulated systemic input data for the model in a 'healthy' and 'impaired' state. By analysing both of these datasets, we show that different parameter spaces can be distinguished and compared between different physiological states or conditions. Finally, we analyse experimental data using the new Bayesian framework and the previous maximum likelihood estimate approach, showing that the Bayesian approach provides a more complete understanding of the parameter space