Reconstructability of Epistatic Functions

Background: Reconstructability Analysis (RA) has been used to detect epistasis in genomic data; in that work, even the simplest RA models (variable-based models without loops) gave performance superior to two other methods. A follow-on theoretical study showed that RA also offers higher-resolution models, namely variable-based models with loops and state-based models, likely to be even more effective in modeling epistasis, and also described several mathematical approaches to classifying types of epistasis. Methods: The present paper extends this second study by discussing a non-standard use of RA: the analysis of epistasis in quantitative as opposed to nominal variables; such quantitative variables are, for example, encountered in genetic characterizations of gene expression, e.g., eQTL data. Three methods are investigated for applying variable- and state-based RA to quantitative dependent variables: (i) k-systems analysis, which treats continuous function values as pseudofrequencies, (ii) b-systems analysis, which derives continuous values from binned DVs using expected value calculations, and (iii) u-systems analysis, which treats continuous function values as pseudo-utilities subject to a lottery. These methods are demonstrated and compared on synthetic data. Results: The three methods of k-, b-, and u-systems analyses, both variable-based and state-based, are then applied to a published SNP dataset. A preliminary search is done with b-systems analysis, followed by more refined k- and u-systems searches. The analyses suggest candidates for epistatic interactions that affect the level of gene expression. As in the synthetic data studies, state-based RA is more powerful than variable-based RA. Conclusions: While the previous RA studies looked at epistasis in nominal (or discretized) data, this paper shows that RA can also analyze epistasis in quantitative expression data without discretizing this data. Since RA can also model epistasis in frequency distributions and detect linkage disequilibrium, its successful application here also to continuous functions shows that it offers a flexible methodology for the analysis of genomic interaction effects

Creating Clinically Useful \u3ci\u3eIn Silico\u3c/i\u3e Models of Intracranial Pressure Dynamics

To create clinically useful computer simulation models of intracranial pressure (ICP) dynamics by using prospective clinical data to estimate subject-specific physiologic parameters

The Role of Environmental Dynamics in the Emergence of Autocatalytic Networks

For life to arise from non-life, a metabolism must emerge and maintain itself, distinct from its environment. One line of research seeking to understand this emergence has focused on models of autocatalytic reaction networks (ARNs) and the conditions that allow them to approximate metabolic behavior. These models have identified reaction parameters from which a proto-metabolism might emerge given an adequate matter-energy flow through the system. This dissertation extends that research by answering the question: can dynamically structured interactions with the environment promote the emergence of ARNs? This question was inspired by theories that place the origin of life in contexts such as diurnal or tidal cycles. To answer it, an artificial chemistry system with ARN potential was implemented in the dissipative particle dynamics (DPD) modeling paradigm. Unlike differential equation (DE) models favored in prior ARN research, the DPD model is able to simulate environmental dynamics interacting with discrete particles, spatial heterogeneity, and rare events. This dissertation first presents a comparison of the DPD model to published DE results, showing qualitative similarity with some interesting differences. Multiple examples are then provided of dynamically changing flows from the environment that promote emergent ARNs more than constant flows. These include specific cycles of energy and mass flux that consistently increase metrics for ARN concentration and mass focusing. The results also demonstrate interesting nonlinear interactions between the system and cycle amplitude and period. These findings demonstrate the relevance that environmental dynamics has to ARN research and the potential for broader application as well

Estimation of Subject Specific ICP Dynamic Models Using Prospective Clinical Data

We used a prospective clinical trial to generate physiologic data to create subject-specific in silico (computer simulation) models of intracranial pressure dynamics in children with severe traumatic brain injury. The trial included a physiologic challenge protocol with changes in head-of-bed elevation and minute ventilation, applied over multiple iterations to three subjects. Physiologic signals (electrocardiogram, respiration, arterial blood pressure, intracranial pressure [ICP], oxygen saturation) were recorded continuously, along with clinical annotations indicating the precise timing of physiologic challenges. Several parameters within the model of ICP dynamics were estimated for each subject based on the ICP response to the challenges. Estimation was done using a standard optimization algorithm to minimize the difference between the ICP trajectory predicted by the model and the actual ICP data. The ICP trajectory predicted by the model was similar to the actual ICP data in all cases, and the mean absolute error varied between 0.5 - 2.8 mmHg (mean = 1.4mmHg). These results demonstrate the potential for using clinically annotated prospective data to create subject-specific computer simulation models. Future research will focus on improvements in the logic for cerebral autoregulatory mechanisms and physiologic adaptation

Reproducing Published Results from \u3ci\u3eIn Silico\u3c/i\u3e Computer Models of the Acute Inflammatory Response to Severe Sepsis

Recent studies describe computer simulation models of the acute or systemic inflammatory response (AIR or SIR) to severe sepsis, a condition that can lead to multiple organ failure and death. One study used an agent-based model, while the other used differential equations (DEs) to simulate a randomized clinical trial. Both studies obtained results similar to the actual results from a successful clinical drug trial of severe sepsis, suggesting that in silico (simulated) randomized clinical trials may be used to design more effective in vivo clinical trials

A Tale of Two Methods—Agent-based Simulation and System Dynamics— Applied in a Biomedical Context: Acute Inflammatory Response

Three specific models of the acute inflammatory response were contrasted. The first model was a recently published and rather complex agent-based model used to simulate clinical trials in silico. The second model was a highly simplified system dynamics model developed during the present research. The third model was also recently published, with similar objectives to the first model, but utilized a complex set of 18 differential equations. The study found that the complexity of the first and third models is likely to adversely impact their usefulness, at least for other researchers. The second model, which is too simple to be used for predictive purposes, shows potential promise as a pedagogical tool, and possibly as the foundation for a somewhat more realistic model that would still be much less complex than the other two models. A comparison table contrasts the three models/methods in more detail. The message for practitioners is one of caution--it is likely to take a considerable period of time to fully realize the potential promise of in silico methods such as those published recently

A Tale of Two Methods—Agent-based Simulation and System Dynamics— Applied in a Biomedical Context: Acute Inflammatory Response

Three specific models of the acute inflammatory response were contrasted. The first model was a recently published and rather complex agent-based model used to simulate clinical trials in silico. The second model was a highly simplified system dynamics model developed during the present research. The third model was also recently published, with similar objectives to the first model, but utilized a complex set of 18 differential equations. The study found that the complexity of the first and third models is likely to adversely impact their usefulness, at least for other researchers. The second model, which is too simple to be used for predictive purposes, shows potential promise as a pedagogical tool, and possibly as the foundation for a somewhat more realistic model that would still be much less complex than the other two models. A comparison table contrasts the three models/methods in more detail. The message for practitioners is one of caution--it is likely to take a considerable period of time to fully realize the potential promise of in silico methods such as those published recently. Problem statement and significance An exciting and relatively new research area is the use of agent based simulation (ABS) models and system dynamic (SD) or differential equation (DE) models to study complex biomedical phenomena such as the systemic inflammatory response syndrome (SIRS), the acut