Increasing/decreasing PDI levels would only decrease/increase, respectively, UscFv levels, but the flux would remain the same

Increasing/decreasing PDI levels would only decrease/increase, respectively, UscFv levels, but the flux would remain the same. INTRODUCTION In systems biology, mathematical models are used to describe biological systems to obtain understanding of system behavior and predict system responses (1). The type of model used and its scale and scope vary with the desired behaviors and responses it is intended to capture and predict, the desired level of detail, and the size of the biological system of interest. Model types range from the highest-level regulatory graphs, which show how species interact, to Bayesian networks, which represent conditional interactions and dependencies, to Boolean models, which describe switching behavior, to nonlinear ODE models, which describe dynamic behavior, to the most highly detailed stochastic models, which capture random behavior caused by low molecule counts (2C4). Model scale may range from molecular to organismal, and from low-level mechanistic detail to higher-level lumped behavioral units. Model building on the mechanistic scale has been referred to as bottom-up, as the model includes previously-known interactions and regulatory feedbacks, which are pared down as analysis identifies the critical, behavior-defining ones. Building on the more abstract, lumped behavioral scale has been referred to as top-down, where input-output relations are used to identify and gradually fill in previously unknown interactions (5). This work combines these two approaches by applying the top-down methodology to biological model building on the mechanistic scale. By and large, mechanistic modeling approaches have not been formalized and are as varied as the models and biological systems under study themselves. Additionally, GENZ-644282 no formal evaluation of the approaches’ applicability to or advantages in modeling a particular biological system has been performed. The body of GENZ-644282 circadian rhythm mathematical models demonstrates the variety of approaches that have been employed to describe a system largely conserved across mammals and fruit flies. In developing their mathematical model for the mammalian circadian rhythm, Forger and Peskin (6) performed an exhaustive literature search to include many of the known molecular interactions and mechanisms involved in the circadian clock, when a basic negative feedback loop was all that was necessary to reproduce experimentally observed oscillations. This approach is clearly in the vein of bottom-up model building, and it produced a mathematical model containing 73 state variables (biological species) and 74 parameters. In stark contrast, Tyson et al. (7) sought to capture and analyze circadian behavior in with a higher-level model by reducing a three-state model consisting of mRNA and two forms (monomer and dimer) of protein to two: mRNA and total protein. Meantime, Leloup and Goldbeter developed 10-state Rabbit polyclonal to MAP1LC3A (8) and 19-state mammalian (9) models of intermediate complexity to fulfill their analytical purposes. Still, one generalized approach to mechanistic modeling of biological systems has been proposed (10): start by identifying all of the reactions within the scope of the biological system and GENZ-644282 perform mass balances around the participating species. Then, simplify the resulting mathematical model consisting of a set of nonlinear ODEs with further assumptions and approximations, which often leads to algebraic expressions, Michaelis-Menten kinetics, and transfer functions such as the Hill function. Finally, employ analytical tools such as sensitivity analysis to identify components responsible for producing certain behaviors and stability and bifurcation analysis to assess what behaviors the system is capable of producing. This process description formalizes the bottom-up approach to mechanistic model building. This work describes a contrasting approach similar to that outlined by Ideker and Lauffenburger (11), but on the scale of mechanistic modeling: with the full desired mechanistic scope of the model defined, develop the simplest imaginable representation of the biological system in an attempt to isolate the backbone structure and identify motifs responsible for.