Optimal regulation of blood glucose level in Type I diabetes using insulin and glucagon

Zebrafish metabolomics: a comprehensive approach to understanding health and disease

Zebrafish (Danio rerio) have become a valuable model in biomedical research due to their genetic similarity to humans, rapid development, and suitability for high-throughput studies. Metabolomic analyses in zebrafish provide critical insights into the biochemical pathways underlying health and disease. This review explores the applications of metabolomics in zebrafish research, highlighting its contributions to understanding embryonic development, tuberculosis, neurodegenerative disorders such as Alzheimer's disease, obesity-related metabolic dysfunction, and drug-induced toxicity through a thorough literature review. Zebrafish metabolomics reveals dynamic metabolite shifts during vertebrate development. In tuberculosis research, zebrafish models have helped identify metabolic biomarkers with potential translational relevance. Studies on Alzheimer's disease suggest that metabolomics can elucidate neuroprotective mechanisms, while investigations into obesity have provided insights into metabolic imbalances associated with kidney dysfunction. Furthermore, toxicometabolomic studies have demonstrated the utility of zebrafish in assessing drug-induced renal injury. Despite their advantages, zebrafish metabolomics faces challenges, including differences in metabolic rates compared to mammals, the need for standardized protocols, and limitations in metabolite database annotations. Nonetheless, integrating metabolomics with other omics approaches holds great promise for advancing disease research and paving the way for personalized medicine.

Evolution of biosynthetic human insulin and its analogues for diabetes management

Hormones play a crucial role in maintaining the normal human physiology. By acting as chemical messengers that facilitate the communication between different organs, tissues and cells of the body hormones assist in responding appropriately to external and internal stimuli that trigger growth, development and metabolic activities of the body. Any abnormalities in the hormonal composition and balance can lead to devastating health consequences. Hormones have been important therapeutic agents since the early 20th century, when it was realized that their exogenous supply could serve as a functional substitution for those hormones which are not produced enough or are completely lacking, endogenously. Insulin, the pivotal anabolic hormone in the body, was used for the treatment of diabetes mellitus, a metabolic disorder due to the absence or intolerance towards insulin, since 1921 and is the trailblazer in hormone therapeutics. At present the largest market share for therapeutic hormones is held by insulin. Many other hormones were introduced into clinical practice following the success with insulin. However, for the six decades following the introduction the first therapeutic hormone, there was no reliable method for producing human hormones. The most common source for hormones were animals, although semisynthetic and synthetic hormones were also developed. However, none of these were optimal because of their allergenicity, immunogenicity, lack of consistency in purity and most importantly, scalability. The advent of recombinant DNA technology was a game changer for hormone therapeutics. This revolutionary molecular biology tool made it possible to synthesize human hormones in microbial cell factories. The approach allowed for the synthesis of highly pure hormones which were structurally and biochemically identical to the human hormones. Further, the fermentation techniques utilized to produce recombinant hormones were highly scalable. Moreover, by employing tools such as site directed mutagenesis along with recombinant DNA technology, it became possible to amend the molecular structure of the hormones to achieve better efficacy and mimic the exact physiology of the endogenous hormone. The first recombinant hormone to be deployed in clinical practice was insulin. It was called biosynthetic human insulin to reflect the biological route of production. Subsequently, the biochemistry of recombinant insulin was modified using the possibilities of recombinant DNA technology and genetic engineering to produce analogues that better mimic physiological insulin. These analogues were tailored to exhibit pharmacokinetic and pharmacodynamic properties of the prandial and basal human insulins to achieve better glycemic control. The present chapter explores the principles of genetic engineering applied to therapeutic hormones by reviewing the evolution of therapeutic insulin and its analogues. It also focuses on how recombinant analogues account for the better management of diabetes mellitus.

The Role of Glucagon in Glycemic Variability in Type 1 Diabetes: A Narrative Review

Type 1 diabetes mellitus (T1DM) is a progressive disease as a result of the severe destruction of islet β-cell function, which leads to high glucose variability in patients. However, α-cell function is also compromised in patients with T1DM, characterized by aberrant fasting and postprandial glucagon secretion. According to recent studies, this aberrant glucagon secretion plays an increasing role in hyperglycemia, insulin-induced hypoglycemia and exercise-associated hypoglycemia in patients with T1DM. With application of continuous glucose monitoring system, dozens of metrics enable the assessment of glycemic variability, which is an integral component of glycemic control for patients with T1DM. There is growing evidences to illustrate the contribution of glucagon secretion to the glycemic variability in patients with T1DM, which may promote the development of new treatment strategies aiming to mitigate glycemic variability associated with aberrant glucagon secretion.

Controlling network ensembles

The field of optimal control typically requires the assumption of perfect knowledge of the system one desires to control, which is an unrealistic assumption for biological systems, or networks, typically affected by high levels of uncertainty. Here, we investigate the minimum energy control of network ensembles, which may take one of a number of possible realizations. We ensure the controller derived can perform the desired control with a tunable amount of accuracy and we study how the control energy and the overall control cost scale with the number of possible realizations. Our focus is in characterizing the solution of the optimal control problem in the limit in which the systems are drawn from a continuous distribution, and in particular, how to properly pose the weighting terms in the objective function. We verify the theory in three examples of interest: a unidirectional chain network with uncertain edge weights and self-loop weights, a network where each edge weight is drawn from a given distribution, and the Jacobian of the dynamics corresponding to the cell signaling network of autophagy in the presence of uncertain parameters.

Application of the control usually requires complete knowledge of the system, which is rare for biological networks characterized by uncertainty. Klickstein et al. propose an optimal control for uncertain systems represented by network ensembles where only weight distributions for edges are known.

Data-driven optimized control of the COVID-19 epidemics

Optimizing the impact on the economy of control strategies aiming at containing the spread of COVID-19 is a critical challenge. We use daily new case counts of COVID-19 patients reported by local health administrations from different Metropolitan Statistical Areas (MSAs) within the US to parametrize a model that well describes the propagation of the disease in each area. We then introduce a time-varying control input that represents the level of social distancing imposed on the population of a given area and solve an optimal control problem with the goal of minimizing the impact of social distancing on the economy in the presence of relevant constraints, such as a desired level of suppression for the epidemics at a terminal time. We find that with the exception of the initial time and of the final time, the optimal control input is well approximated by a constant, specific to each area, which contrasts with the implemented system of reopening 'in phases'. For all the areas considered, this optimal level corresponds to stricter social distancing than the level estimated from data. Proper selection of the time period for application of the control action optimally is important: depending on the particular MSA this period should be either short or long or intermediate. We also consider the case that the transmissibility increases in time (due e.g. to increasingly colder weather), for which we find that the optimal control solution yields progressively stricter measures of social distancing. We finally compute the optimal control solution for a model modified to incorporate the effects of vaccinations on the population and we see that depending on a number of factors, social distancing measures could be optimally reduced during the period over which vaccines are administered to the population.

Mathematical Model of Glucagon Kinetics for the Assessment of Insulin-Mediated Glucagon Inhibition During an Oral Glucose Tolerance Test

Glucagon is secreted from the pancreatic alpha cells and plays an important role in the maintenance of glucose homeostasis, by interacting with insulin. The plasma glucose levels determine whether glucagon secretion or insulin secretion is activated or inhibited. Despite its relevance, some aspects of glucagon secretion and kinetics remain unclear. To gain insight into this, we aimed to develop a mathematical model of the glucagon kinetics during an oral glucose tolerance test, which is sufficiently simple to be used in the clinical practice. The proposed model included two first-order differential equations -one describing glucagon and the other describing C-peptide in a compartment remote from plasma - and yielded a parameter of possible clinical relevance (i.e., S_GLUCA(t), glucagon-inhibition sensitivity to glucose-induced insulin secretion). Model was validated on mean glucagon data derived from the scientific literature, yielding values for S_GLUCA(t) ranging from -15.03 to 2.75 (ng of glucagon·nmol of C-peptide^-1). A further validation on a total of 100 virtual subjects provided reliable results (mean residuals between -1.5 and 1.5 ng·L^-1) and a negative significant linear correlation (r = -0.74, p < 0.0001, 95% CI: -0.82 - -0.64) between S_GLUCA(t) and the ratio between the areas under the curve of suprabasal remote C-peptide and glucagon. Model reliability was also proven by the ability to capture different patterns in glucagon kinetics. In conclusion, the proposed model reliably reproduces glucagon kinetics and is characterized by sufficient simplicity to be possibly used in the clinical practice, for the estimation in the single individual of some glucagon-related parameters.