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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.

Neonatal outcomes have improved over the last decade following significant thrust in this area, but stillbirth, preterm birth and neonatal brain injury remain acute global problems with long-lasting parental and family psychological trauma. In 2020, 1 in every 225 pregnancies in UK ended in stillbirth, with 2 million stillbirths reported worldwide. Over 40% of all stillbirths occur during labor—a loss that could be avoided with improved fetal monitoring and timely access to emergency obstetric care when required. Nearly one-fourth of global neonatal mortality relates to intrapartum-related events. Currently, available monitoring tools rely on surrogate markers such as serial fetal size measurement, doppler assessment of fetoplacental perfusion, fetal heart rate variability, fetal movements and maternal circulating placental proteins to identify the vulnerable fetus. Continuous cardiotocography (CTG) is the current standard of monitoring for fetal assessment in labor, but a Cochrane review indicated that it failed to significantly reduce poor outcomes in newborn infants, and resulted in an increase in the number of Caesarean sections. There is an urgent need for the development of a monitoring platform to directly measure acute or chronic changes related to fetoplacental compromise which can be operated with ease both in the hospital and remotely in the home environment in high-risk pregnancies. In recent years, there has been some promising development to identify compromised fetuses using advanced technologies and artificial intelligence-based approaches. We present here the current state of fetoplacental monitoring, focussing primarily on antepartum monitoring and discuss a possible way forward using digital biomarkers in this area to protect babies and mothers in future.

Multimodal monitoring of the brain generates a great quantity of data, providing the potential for great insight into both healthy and injured cerebral dynamics. In particular, near-infrared spectroscopy can be used to measure various physiological variables of interest, such as haemoglobin oxygenation and the redox state of cytochrome-c-oxidase, alongside systemic signals, such as blood pressure. Interpreting these measurements is a complex endeavour, and much work has been done to develop mathematical models that can help to provide understanding of the underlying processes that contribute to the overall dynamics. BCMD is a software framework that was developed to run such models. However, obtaining, installing and running this software is no simple task. Here we present WeBCMD, an online environment that attempts to make the process simpler and much more accessible. By leveraging modern web technologies, an extensible and cross-platform package has been created that can also be accessed remotely from the cloud. WeBCMD is available as a Docker image and an online service.

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.

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 a more complete understanding of the parameter space 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. Footnotes * Autogenerated PDF from PLOS had errors in how figures were displayed and ordered. This version simply places Figures directly into the PDF so as to ensure correct ordering.