The cell is a highly ordered and intricate machine within which a wide variety of chemical processes take place. The full scientific understanding of cellular physiology requires accurate mathematical models that depict the temporal dynamics of these chemical processes. Modelers build mathematical models of chemical processes primarily from systems of differential equations. Although developing new biological ideas is more of an art than a science, constructing a mathematical model from a biological idea is largely mechanical and automatable. This dissertation describes the practices and processes that biological modelers use for modeling and simulation. Computational biologists struggle with existing tools for creating models of complex eukaryotic cells. This dissertation develops new processes for biological modeling that make model creation, verification, validation, and testing less of a struggle. This dissertation introduces computational software that automates parts of the biological modeling process, including model building, transformation, execution, analysis, and evaluation. User and methodological requirements heavily affect the suitability of software for biological modeling. This dissertation examines the modeling software in terms of these requirements. Intelligent, automated model evaluation shows a tremendous potential to enable the rapid, repeatable, and cost-effective development of accurate models. This dissertation presents a case study that indicates that automated model evaluation can reduce the evaluation time for a budding yeast model from several hours to a few seconds, representing a more than 1000-fold improvement. Although constructing an automated model evaluation procedure requires considerable domain expertise and skill in modeling and simulation, applying an existing automated model evaluation procedure does not. With this automated model evaluation procedure, the computer can then search for and potentially discover models superior to those that the biological modelers developed previously. / Ph. D.

Mathematical models of regulatory networks become more difficult to construct and understand as they grow in size and complexity. Large regulatory network models can be built up from smaller models, representing subsets of reactions within the larger network. This dissertation focuses on novel model construction techniques that extend the ability of biological modelers to construct larger models by supplying them with tools for decomposing models and using the resulting components to construct larger models. Over the last 20 years, molecular biologists have amassed a great deal of information about the genes and proteins that carry out fundamental biological processes within living cells --- processes such as growth and reproduction, movement, signal reception and response, and programmed cell death. The full complexity of these macromolecular regulatory networks is too great to tackle mathematically at the present time. Nonetheless, modelers have had success building dynamical models of restricted parts of the network. Systems biologists need tools now to support composing "submodels" into more comprehensive models of integrated regulatory networks. We have identified and developed four novel processes (fusion, composition, flattening, and aggregation) whose purpose is to support the construction of larger models. Model Fusion combines two or more models in an irreversible manner. In fusion, the identities of the original (sub)models are lost. Beyond some size, fused models will become too complex to grasp and manage as single entities. In this case, it may be more useful to represent large models as compositions of distinct components. In Model Composition one thinks of models not as monolithic entities but rather as collections of smaller components (submodels) joined together. A composed model is built from two or more submodels by describing their redundancies and interactions. While it is appealing in the short term to build larger models from pre-existing models, each developed independently for their own purposes, we believe that ultimately it will become necessary to build large models from components that have been designed for the purpose of combining them. We define Model Aggregation as a restricted form of composition that represents a collection of model elements as a single entity (a "module"). A module contains a definition of pre-determined input and output ports. The process of aggregation (connecting modules via their interface ports) allows modelers to create larger models in a controlled manner. Model Flattening converts a composed or aggregated model with some hierarchy or connections to one without such connections. The relationships used to describe the interactions among the submodels are lost, as the composed or aggregated model is converted into a single large (flat) model. Flattening allows us to use existing simulation tools, which have no support for composition or aggregation. / Ph. D.

By increasing the level of abstraction in the representation of regulatory network models, we can hope to allow modelers to create models that are beyond the threshold of what can currently be expressed reliably. As hundreds of reactions are difficult to understand, maintain, and extend, thousands of reactions become next to impossible without any automation or aid. Using the multistate-species concept we can reduce the number of reactions needed to represent certain systems and thus, lessen the cognitive load on modelers. A multistate species is an entity with a defined range for state variables, which refers to a group of different forms for a specific species. A multistate reaction involves one or more multistate species and compactly represents a group of similar single reactions. In this work, we have extended JCMB (the JigCell Model Builder) to comply with multistate species and reactions modeling and presented a proposal for enhancing SBML (the Systems Biology Markup Language) standards to support multistate models. / Master of Science

The ever-growing size and complexity of molecular network models makes them difficult to construct and understand. Modifying a model that consists of tens of reactions is no easy task. Attempting the same on a model containing hundreds of reactions can seem nearly impossible. We present the JigCell Model Connector, a software tool that supports large-scale molecular network modeling. Our approach to developing large models is to combine together smaller models, making the result easier to comprehend. At the base, the smaller models (called modules) are defined by small collections of reactions. Modules connect together to form larger modules through clearly defined interfaces, called ports. In this work, we enhance the port concept by defining different types of ports. Not all modules connect together the same way, therefore multiple connection options need to exist. / Master of Science