Quantitative modeling, analysis, prediction and interpretation are valuable interdisciplinary tools for understanding complex biological systems that can be provided by mathematics. This study is dedicated to the application of mathematics in biology and is based on an interplay of differential equations, dynamical systems, graph theory, network mathematics and computational modeling. Both the temporal (time) changes of population sizes, interaction between cells, and physiological processes can be modeled as differential equations, and graph-based models can be used to capture the overall structure of complex biological networks and their connectivity. Biological processes which involve growth, regulation, competition and interaction are also represented using mathematical models. The behavior of gene expression is investigated with quantitative formulations, in order to understand the molecular dynamics and regulation. Estimation of disease transmission, development and the effects of interventions are also performed using disease progression models. The interdisciplinary approach shows the advantages of a mathematical literacy in enhancing biological interpretations, enabling prediction, and also the development of systematic mathematical tools for exploring and understanding complex biological mechanisms at several spatial and temporal scales.