This communication provides an illustration for the use of computer simulations in human immunology. When traditional experiments are impossible, unethical, or unfeasible, in silico modeling procedures may help to fill the gaps in our knowledge of an immune system response to a pathogen. In our study, we define terms and properties of modeled entities: “a clonotype”, its distribution, and rank-frequency summaries, and describe properties associated with each of these three clonotype-related entities. We simulate a multistage dynamic process of an immune memory response to influenza. We believe that illustrated properties of fractality and self-similarity might arise due to the following process. The memory T cells operate in a complex environment of shifting pathogen concentrations, increasing and then decreasing inflammatory signals, and multiple interactions with other immune cells and their infected targets. Therefore, a fractal structure to such a population would represent an optimization in terms of percolation into immune/inflammatory space.

Computational models have been successfully applied to a wide variety of research areas including infectious disease epidemiology. Especially for questions that are difficult to examine in other ways, computational models have been used to extend the range of epidemiological issues that can be addressed, advance theoretical understanding of disease processes and help identify specific intervention strategies. We explore each of these contributions to epidemiology research through discussion and examples. We also describe in detail models for raccoon rabies and methicillin-resistant Staphylococcus aureus, drawn from our own research, to further illustrate the role of computation in epidemiological modeling.

The human brain is one of the most complex biological systems. Neuro scientists seek to understand the brain function through detailed analysis of neuronal excitability and synaptic transmission. In this study, we propose a network analysis framework to study the evolution of epileptic seizures. We apply a signal processing approach, derived from information theory, to investigate the synchronization of neuronal activities, which can be captured by electroencephalogram (EEG) recordings. Two network-theoretic approaches are proposed to globally model the synchronization of the brain network. We observe some unique patterns related to the development of epileptic seizures, which can be used to illuminate the brain function governed by the epileptogenic process during the period before a seizure. The proposed framework can provide a global structural patterns in the brain network and may be used in the simulation study of dynamical systems (e.g. the brain) to predict oncoming events (e.g. seizures). To analyze long-term EEG recordings in the future, we discuss how the Markov-Chain Monte Carlo (MCMC) methodology can be applied to estimate the clique parameters. This MCMC framework fits very well with this work as the epileptic evolution can be considered to be a system with unobservable state variables and nonlinearities.

The dynamics of a self-organised model of shoaling fish are explored within a Lagrangian (or individual based) framework in order to identify the key behavioural factors that shape its dynamic landscape. By exploring systematically all possible initial states we identify the transitions to and between the different possible stationary states (schooling vs. swarming or milling). The route to these stationary states is explained from an individual perspective. On the behavioural level we discuss in particular the decisive impact of two traits, the perception angle and the manoeuvrability of the fish. A key result of this study is that the fish density in certain stationary states reaches values where each fish perceives each other; local interactions actually become global interactions. We further discuss the specific value of such Lagrangian studies in comparison to analytical approaches, in particular the freedom to include any important biological trait and the importance of an exhaustive numerical investigation.

Population-based genetic association studies are increasingly used to explore the association between genetic polymorphisms and outcomes such as disease-status and disease-related quantitative traits. Because multiple polymorphisms are typically available, there are several statistical analysis strategies that might be appropriate depending on the goal of the study. In this paper, we compare several linear model parameterizations that might be used to perform a test of association between a genomic region defined by multiple SNPs and a quantitative trait. We compare via simulation the type I error and power of the omnibus F-test to detect association. As expected, there is no one most powerful test across the genetic models we considered, although tests based on simple parameterizations that do not rely on phase information can be as powerful as more complicated haplotype-based tests even when it is a haplotype that is truly associated with the trait.

Constant re-evaluation of social affiliation is known to cause populations of individuals with different predetermined affiliation preferences to diverge into different network structures. In this study, rather than assigning to each individual a fixed affiliation preference, held throughout the duration of the dynamic network evolution, individuals were allowed an initial “learning period” during which they compared their own relative success, using each of three strategies, at maximizing their social status under three different metrics. Based on the outcomes from this learning period, individuals then chose one particular strategy. The organizational success and stability of the resulting populations was seen to be higher than those of the populations of individuals whose behaviors were predetermined. This indicates that individual-level evaluation and strategy choice in social affiliation preferences can yield strong benefits to the organizational success of the population as a whole.

School closure at the outset of epidemic outbreaks has been recommended as one of the best ways to protect children and prevent amplifying the outbreak by gathering susceptible individuals, with relatively poor hygiene, into close contact, and then sending them back out to mix with society at large. However, school closure is not without its own, potentially critical, impact on the function of society. Outbreak-related workforce depletion is already another major concern of pandemic preparedness planners, and caring for children during the day may drastically contribute to adult absenteeism from work. We present a series of computational models to examine whether alternative in-school strategies could provide some measure of infection control without producing the same societal burden in finding alternative childcare. These investigations lead to the conclusion that some non-closure options may provide the best societal protection, finding an appropriate balance between preventing further infection and compromising general societal function.

The inference of the interactions between organisms in an ecosystem from observational data is an important problem in ecology. This paper presents a mathematical inference method, originally developed for the inference of biochemical networks in molecular biology, adapted for the inference of networks of ecological interactions. The method is applied to a network of invertebrate families (taxa) in a rice field.