Model-Based Hypothesis Testing in Biomedicine : How Systems Biology Can Drive the Growth of Scientific Knowledge

Abstract

The utilization of mathematical tools within biology and medicine has traditionally been less widespread compared to other hard sciences, such as physics and chemistry. However, an increased need for tools such as data processing, bioinformatics, statistics, and mathematical modeling, have emerged due to advancements during the last decades. These advancements are partly due to the development of high-throughput experimental procedures and techniques, which produce ever increasing amounts of data. For all aspects of biology and medicine, these data reveal a high level of inter-connectivity between components, which operate on many levels of control, and with multiple feedbacks both between and within each level of control. However, the availability of these large-scale data is not synonymous to a detailed mechanistic understanding of the underlying system. Rather, a mechanistic understanding is gained first when we construct a hypothesis, and test its predictions experimentally. Identifying interesting predictions that are quantitative in nature, generally requires mathematical modeling. This, in turn, requires that the studied system can be formulated into a mathematical model, such as a series of ordinary differential equations, where different hypotheses can be expressed as precise mathematical expressions that influence the output of the model.Within specific sub-domains of biology, the utilization of mathematical models have had a long tradition, such as the modeling done on electrophysiology by Hodgkin and Huxley in the 1950s. However, it is only in recent years, with the arrival of the field known as systems biology that mathematical modeling has become more commonplace. The somewhat slow adaptation of mathematical modeling in biology is partly due to historical differences in training and terminology, as well as in a lack of awareness of showcases illustrating how modeling can make a difference, or even be required, for a correct analysis of the experimental data.In this work, I provide such showcases by demonstrating the universality and applicability of mathematical modeling and hypothesis testing in three disparate biological systems. In Paper II, we demonstrate how mathematical modeling is necessary for the correct interpretation and analysis of dominant negative inhibition data in insulin signaling in primary human adipocytes. In Paper III, we use modeling to determine transport rates across the nuclear membrane in yeast cells, and we show how this technique is superior to traditional curve-fitting methods. We also demonstrate the issue of population heterogeneity and the need to account for individual differences between cells and the population at large. In Paper IV, we use mathematical modeling to reject three hypotheses concerning the phenomenon of facilitation in pyramidal nerve cells in rats and mice. We also show how one surviving hypothesis can explain all data and adequately describe independent validation data. Finally, in Paper I, we develop a method for model selection and discri...

Mathematical modeling of neurovascular coupling

“…An new interdisciplinary research field -Systems Biology -emerged in the early 2000s [36][37][38][39]. Systems Biology aims to provide a systems-level wide understanding of biological systems, and to capture how underlying system properties arise only when combining individual pieces from the same puzzle or system.…”

“…Systems Biology aims to provide a systems-level wide understanding of biological systems, and to capture how underlying system properties arise only when combining individual pieces from the same puzzle or system. This stands in contrast to the classical reductionist approach, where each component (or puzzle piece) is examined in isolation, a common approach in in vitro molecular biology [39]. Systems Biology rests on two core pillars: 1) acquisition of experimental data from the intact biological system, and 2) the use of mathematical models to analyze that data to generate new insights regarding the system.…”

“…The second concept, mathematical modeling, is useful for analyzing complex data sets as it provides a framework for quantitatively testing hypotheses about the inner workings of the system, and in turn, how the captured experimental data was generated. As Systems Biology aims to capture all of the pieces of a given system, which are often complex and interconnected, the generated models and explanations are often also complex [36,39]. This is typically the case for mechanistic modeling, where the components of the model, as well as the system properties that arise from the inter-connected components, can be understood in biological terms.…”

“…This modeling cycle constitutes the practical framework for model construction, model-based hypothesis testing and data analysis, and finally, model predictions and modelderived insights or decisions. This framework is valid on two scales: on a small scale, concerning the individual modeler who performs the analyses, and on a larger group scale, suggesting how to organize an iterative collaboration between experimentalists who gather data and modelers who make the models [39].…”

“…In the previous section, the topic of optimization and finding the parametersp which minimize the discrepancy between observed experimental data and model simulations was covered. We now want to test if the discrepancies between model and data are small enough, i.e., assess the goodness of fit in order to determine whether to accept or reject the model [39]. This is 4.6.…”