Modeling human muscle metabolism: using constraint-based modeling to investigate nutrition supplements, insulin resistance, and type 2 diabetes

Human muscle metabolism, the biochemical reactions which lead storage and usage of energy, is complex, but important in understanding human health and disease. Optimal muscle metabolism can help maintain a healthy organism by adequately storing and utilizing energy for subsequent use in contraction and recovery and adaption from contraction and exercise. Dysregulated muscle metabolism can lead to insulin resistance and obesity among other health problems. Flux balance analysis (FBA) and constraint-based modeling have successfully elucidated important aspects of metabolism in single-celled organisms. However, limited work has been done with multicellular organisms. The foci of this dissertation are (1) to show how novel applications of this technique can aid in the investigation of human nutrition and (2) to elucidate metabolic phenotypes associated with the insulin resistance (IR) characteristics of Type 2 Diabetes (T2D). First, for human nutrition a novel steady-state constraint-based model of skeletal muscle tissue was constructed to investigate the effect of amino acid supplementation on protein synthesis. Several in silico supplementation strategies implemented showed that amino acid supplementation could increase muscle contractile protein synthesis, which is consistent with published data on protein synthesis in a post-resistance exercise state. These results suggest that increasing bioavailability of methionine, arginine, and the branched-chain amino acids can increase the flux of contractile protein synthesis. Thus, this dissertation introduces the prospect of using systems biology as a framework to investigate how supplementation and nutrition can affect human metabolism and physiology. Second, given the complexity of metabolism, the cause(s) of the altered muscle metabolism in IR remain(s) unknown. Attempting to elucidate this complexity, the constraint-based modeling framework was expanded upon to develop the first in silico analysis of muscle metabolism under varying nutrient conditions and during transitions from fasted to fed states. Systematic perturbations of the metabolic network identified reactions which mimic IR phenotypes: reduced ATP/creatine phosphate synthesis, reduced TCA cycle flux, and reduced metabolic flexibility. Reduced flux through a single reaction is not sufficient to recapitulate the IR phenotypes, but knockdowns in pyruvate dehydrogenase in combination with either reduced lipid uptake or lipid/amino acid oxidative metabolism do so. These combinations also decrease complete lipid oxidation and glycogen storage. Thus, the computational model also provides a novel tool to identify candidate enzymes contributing to dysregulated metabolism in IR

To Supplement or Not to Supplement: A Metabolic Network Framework for Human Nutritional Supplements

Flux balance analysis and constraint based modeling have been successfully used in the past to elucidate the metabolism of single cellular organisms. However, limited work has been done with multicellular organisms and even less with humans. The focus of this paper is to present a novel use of this technique by investigating human nutrition, a challenging field of study. Specifically, we present a steady state constraint based model of skeletal muscle tissue to investigate amino acid supplementation's effect on protein synthesis. We implement several in silico supplementation strategies to study whether amino acid supplementation might be beneficial for increasing muscle contractile protein synthesis. Concurrent with published data on amino acid supplementation's effect on protein synthesis in a post resistance exercise state, our results suggest that increasing bioavailability of methionine, arginine, and the branched-chain amino acids can increase the flux of contractile protein synthesis. The study also suggests that a common commercial supplement, glutamine, is not an effective supplement in the context of increasing protein synthesis and thus, muscle mass. Similar to any study in a model organism, the computational modeling of this research has some limitations. Thus, this paper introduces the prospect of using systems biology as a framework to formally investigate how supplementation and nutrition can affect human metabolism and physiology

RTDB: A memory resident real-time object database

RTDB is a fast, memory-resident object database with built-in support for distribution. It constitutes an attractive alternative for architecting real-time solutions with multiple, possibly distributed, processes or agents sharing data. RTDB offers both direct and navigational access to stored objects, with local and remote random access by object identifiers, and immediate direct access via object indices. The database supports transparent access to objects stored in multiple collaborating dispersed databases and includes a built-in cache mechanism that allows for keeping local copies of remote objects, with specifiable invalidation deadlines. Additional features of RTDB include a trigger mechanism on objects that allows for issuing events or activating handlers when objects are accessed or modified and a very fast, attribute based search/query mechanism. The overall architecture and application of RTDB in a control and monitoring system is presented

A framework for constructing adaptive and reconfigurable systems

This paper presents a software approach to augmenting existing real-time systems with self-adaptation capabilities. In this approach, based on the control loop paradigm commonly used in industrial control, self-adaptation is decomposed into observing system events, inferring necessary changes based on a system's functional model, and activating appropriate adaptation procedures. The solution adopts an architectural decomposition that emphasizes independence and separation of concerns. It encapsulates observation, modeling and correction into separate modules to allow for easier customization of the adaptive behavior and flexibility in selecting implementation technologies

Combining structured and unstructured data in a configurable web-based logbook

A typical electronic logbook is designed as a general-purpose system for recording time-ordered events and actions and, therefore, allows for a great flexibility in recording information, but the data is unstructured. To better position it in a specific context (e.g., a, test facility, a group activity log) it needs to support both structured data (keyword, authors, etc) and unstructured data (text, title, attachments) in that context. To do this, a logbook system can define a set of attributes, possibly built as a hierarchy. These application-specific attributes will be associated with each entry. To be flexible, such a system has to be configurable to allow for tailoring it to each specific environment. The paper describes a design, functionality, and experiences with WebLog, a database-configurable electronic logbook developed with the J2EE Web technology. Various functional and technical properties of the system are discussed, including views, searches, threads of entries, an automated alerting system as well as integration with other applications