Hyperglycemia is prevalent in critical care, as patients experience stress-induced hyperglycemia, even with no history of diabetes. Hyperglycemia has a significant impact on patient mortality and other negative clinical outcomes such as severe infection, sepsis and septic shock. Tight glycemic control can significantly reduce these negative outcomes by reducing hyperglycemic episode, but achieving it remains clinically elusive, particularly with regard to what constitutes tight control and what protocols are optimal in terms of results and clinical effort. The model used in this thesis is validated using an independent data and readily be used for different clinical interventions. Moreover, this model also able to accurately predict clinical intervention outcomes given that the model prediction error is very small, which is better than any other reported model. In particular, model-based glycemic control methods is used to capture patient-specific physiological dynamics, such as insulin sensitivity, SI. To date, sepsis diagnosis has been a great challenge despite advancement in technologies and medical research. Critically, septic patients are often classified by practitioners according to their experience before standard test results can be assessed, as to avoid delay in treatment. Moreover, several scoring systems have also been widely used to represent sepsis condition and better standardization of sepsis definition across different centers. In this thesis, insulin sensitivity, SI, a model-based metric is used to identify sepsis condition based on the finding that SI represents metabolic condition of a patient. Additionally, several clinical and physiological variables obtained during patient’s stay in critical care are also investigated using mathematical computation and statistical analysis to identify relevant metric which can be accurately use for sepsis interventions. Even though information on SI, clinical and physiological variables of a patient are insufficient to determine the sepsis status, these informations have brought to a different perspective of diagnosing sepsis. Microcirculation dysfunction is very common in sepsis. Tracking of microcirculation state among septic patient enable better tracking of patient state particularly sepsis status. The tracking can potentially be done by using a pulse oximeter that can extract additional information related to oxygen extraction level. The processed signals are therefore represent relative absorption of oxyhemoglobin and reduced hemoglobin that can be used to assess microcirculation status. In addition, this thesis focus on the real challenge of early treatment of sepsis and sepsis diagnosis where several potential metabolic markers are investigated. Microcirculation conditions are assessed using a non-invasive method that is generally used in typical ICU settings. In particular, the concept and method used to assess microcirculation and metabolic conditions are developed in this thesis. Finally, the work presented in this thesis can act as a starting point for many other glycemic control problems in other environments. These areas include cardiac critical care and neonatal critical care that share most similarities to the environment studied in this thesis, to general diabetes where the population is growing exponentially world wide. Eventually, this added knowledge can lead clinical developments from protocol simulations to better clinical decision making.