BACKGROUND: Computational techniques are becoming increasingly powerful and modeling tools for biological systems are of greater needs. Biological systems are inherently multiscale, from molecules to tissues and from nano-seconds to a lifespan of several years or decades. ENISI MSM integrates multiple modeling technologies to understand immunological processes from signaling pathways within cells to lesion formation at the tissue level. This paper examines and summarizes the technical details of ENISI, from its initial version to its latest cutting-edge implementation. IMPLEMENTATION: Object-oriented programming approach is adopted to develop a suite of tools based on ENISI. Multiple modeling technologies are integrated to visualize tissues, cells as well as proteins; furthermore, performance matching between the scales is addressed. CONCLUSION: We used ENISI MSM for developing predictive multiscale models of the mucosal immune system during gut inflammation. Our modeling predictions dissect the mechanisms by which effector CD4+ T cell responses contribute to tissue damage in the gut mucosa following immune dysregulation.Computational modeling techniques are playing increasingly important roles in advancing a systems-level mechanistic understanding of biological processes. Computer simulations guide and underpin experimental and clinical efforts. This study presents ENteric Immune Simulator (ENISI), a multiscale modeling tool for modeling the mucosal immune responses. ENISI's modeling environment can simulate in silico experiments from molecular signaling pathways to tissue level events such as tissue lesion formation. ENISI's architecture integrates multiple modeling technologies including ABM (agent-based modeling), ODE (ordinary differential equations), SDE (stochastic modeling equations), and PDE (partial differential equations). This paper focuses on the implementation and developmental challenges of ENISI. A multiscale model of mucosal immune responses during colonic inflammation, including CD4+ T cell differentiation and tissue level cell-cell interactions was developed to illustrate the capabilities, power and scope of ENISI MSM.

Modeling and simulations approaches have been widely used in computational biology, mathematics, bioinformatics and engineering to represent complex existing knowledge and to effectively generate novel hypotheses. While deterministic modeling strategies are widely used in computational biology, stochastic modeling techniques are not as popular due to a lack of user-friendly tools. This paper presents ENISI SDE, a novel web-based modeling tool with stochastic differential equations. ENISI SDE provides user-friendly web user interfaces to facilitate adoption by immunologists and computational biologists. This work provides three major contributions: (1) discussion of SDE as a generic approach for stochastic modeling in computational biology; (2) development of ENISI SDE, a web-based user-friendly SDE modeling tool that highly resembles regular ODE-based modeling; (3) applying ENISI SDE modeling tool through a use case for studying stochastic sources of cell heterogeneity in the context of CD4+ T cell differentiation. The CD4+ T cell differential ODE model has been published [8] and can be downloaded from biomodels.net. The case study reproduces a biological phenomenon that is not captured by the previously published ODE model and shows the effectiveness of SDE as a stochastic modeling approach in biology in general and immunology in particular and the power of ENISI SDE.

Biological systems span several orders of magnitude in space and time from intracellular pathways to tissue-level processes. Many studies focus on molecular level events while other studies focus on cellular level and tissue level interactions. The immune system is highly complex and dynamic, encompassing hierarchical interactions with dimensions ranging from nanometers to meters and time scales from nanoseconds to years. To comprehensively model immunological processes computationally, multi-scale models are needed. However, the lack of multi-scale modeling tools can be a deterrent to advance the understanding of the immune system across scales. In this paper, we developed an object-oriented multi-scale modeling (MSM) platform, ENISI MSM, that integrates agent-based modeling (ABM), ordinary-differential equations (ODE), and partial differential equations (PDE) models. To our best knowledge, this is the first such multi-scale modeling platform that is capable of integrating ODE, PDE, and ABM models together. The tool is developed in Java and is object-oriented. For example, the agents are objects and the ODE and PDE solvers are also objects. ENISI MSM also provides user-friendly interfaces and visualizations. We developed a multi-scale CD4+ T cell differentiation model in the context of gut inflammatory and showed the effectiveness of ENISI MSM.

Stochasticity is part of the nature of many biological processes and an important aspect in modeling and simulations of computational biology. Gillespie's algorithm was developed for modeling stochasticity of chemical reactions and has been broadly applied to stochastic modeling in computational biology. The Gillespie's algorithm accounts for the particle effect in chemical reactions. However, many biological processes including cellular and molecular immunological mechanisms have many other sources of stochasticity such as cell movement, ligand binding, or unaccounted variation in experimental settings. In this paper, we propose stochastic differential equations (SDE) being used as a generic stochastic modeling technique for systems immunology. SDE has been widely used in statistics and economics areas; but only a few isolated studies in computational biology are found using SDE to model stochastic behaviors of cells and molecules. In addition, to the best of our knowledge, there is no user-friendly SDE-based modeling tool available for computational biologists. This paper presents ENISI SDE, a web-based user-friendly stochastic modeling tool for computational biologists. This work provides three major contributions: (1) we discuss SDE and propose it as a generic approach for stochastic modeling in computational biology; (2) we develop ENISI SDE, a web-based user-friendly SDE modeling tool that only requires little extra effort beyond regular ODE-based modeling; (3) we use the model SDE modeling tool to study stochastic sources of cell heterogeneity in the context of a CD4+ T Cell differentiation process. The case study clearly shows the effectiveness of SDE as a stochastic modeling approach in biology in general and immunology in particular and the power of the SDE modeling tool we developed.

The immune system is composed of heterogeneous cell populations and it includes several hundreds of distinct cells types such as neutrohils, eosinophils, basophils, macrophages, dendritic cells, CD4+ and CD8+ T cells, 7<5 T cells, mast cells, and B cells and each main cell type can be further differentiated into subsets with unique and overlapping functions. For example CD4+ T cells can be differentiated into T helper (Th)l, Th2, Thl7, and regulatory T cell (Treg) subsets. To study molecular mechanisms of cell differentiation, Systems Biology Markup Language (SBML) based Ordinary Differential Equation (ODE) models can be used for representing such processes. These intracellular signaling models often require many equations to accurately represent intracellular pathways and biochemical reactions. Another challenge in studying the immune system and immune responses is the need for integration of complex processes that occur at different time and space scales (i. e., populations, whole organism, tissue level, cellular and molecular) through multi-scale modeling. This study presents two novel neural network models for modeling CD4+ T cell differentiation and immune cell subset classification. The first model reduces the complex ODE intracellular model by focusing on the input and output cytokines and the second model establishes an automated subset classification based on molecular patterns expressed in immune cells. After training, the first model achieves small prediction errors of cytokine concentrations and the second model achieves 98% prediction rate for subset classification. Neural network algorithm and models have been widely used for many data mining tasks such as classification and pattern recognition. However, to the best of our knowledge this study is the first one applying the neural network algorithm for immune cell differentiation and subset classification. In addition, these novel neural network models can be computationally efficiently integrated into multi-scale models with limited computational costs.

Differentiation of CD4+ T cells into effector or regulatory phenotypes is tightly controlled by the cytokine milieu, complex intracellular signaling networks and numerous transcriptional regulators. We combined experimental approaches and computational modeling to investigate the mechanisms controlling differentiation and plasticity of CD4+ T cells in the gut of mice. Our computational model encompasses the major intracellular pathways involved in CD4+ T cell differentiation into T helper 1 (Th1), Th2, Th17 and induced regulatory T cells (iTreg). Our modeling efforts predicted a critical role for peroxisome proliferator-activated receptor gamma (PPARγ) in modulating plasticity between Th17 and iTreg cells. PPARγ regulates differentiation, activation and cytokine production, thereby controlling the induction of effector and regulatory responses, and is a promising therapeutic target for dysregulated immune responses and inflammation. Our modeling efforts predict that following PPARγ activation, Th17 cells undergo phenotype switch and become iTreg cells. This prediction was validated by results of adoptive transfer studies showing an increase of colonic iTreg and a decrease of Th17 cells in the gut mucosa of mice with colitis following pharmacological activation of PPARγ. Deletion of PPARγ in CD4+ T cells impaired mucosal iTreg and enhanced colitogenic Th17 responses in mice with CD4+ T cell-induced colitis. Thus, for the first time we provide novel molecular evidence in vivo demonstrating that PPARγ in addition to regulating CD4+ T cell differentiation also plays a major role controlling Th17 and iTreg plasticity in the gut mucosa.

This paper presents ENISI Visual, an agent-based simulator for modeling gut immunity to enteric pathogens. Gastrointestinal systems are important for in-taking food and other nutritions and gut immunity is an important part of human immune system. ENISI Visual provides quality visualizations and users can control initial cell concentrations and the simulation speed, take snapshots, and record videos. The cells are represented with different icons and the icons change colors as their states change. Users can observe real-time immune responses, including cell recruitment, cytokine and chemokine secretion and dissipation, random or chemotactic movement, cell-cell interactions, and state changes. The case study clearly shows that users can use ENISI Visual to develop models and run novel and insightful in silico experiments.

Here we present the ENteric Immunity Simulator (ENISI), a modeling system for the inflammatory and regulatory immune pathways triggered by microbe-immune cell interactions in the gut. With ENISI, immunologists and infectious disease experts can test and generate hypotheses for enteric disease pathology and propose interventions through experimental infection of an in silico gut. ENISI is an agent based simulator, in which individual cells move through the simulated tissues, and engage in context-dependent interactions with the other cells with which they are in contact. The scale of ENISI is unprecedented in this domain, with the ability to simulate $10^7$ cells for 250 simulated days on 576 cores in one and a half hours, with the potential to scale to even larger hardware and problem sizes. In this paper we describe the ENISI simulator for modeling mucosal immune responses to gastrointestinal pathogens. We then demonstrate the utility of ENISI by recreating an experimental infection of a mouse with Helicobacter pylori 26695. The results identify specific processes by which bacterial virulence factors do and do not contribute to pathogenesis associated with H. pylori strain 26695. These modeling results inform general intervention strategies by indicating immunomodulatory mechanisms such as those used in inflammatory bowel disease may be more appropriate therapeutically than directly targeting specific microbial populations through vaccination or by using antimicrobials.

Mobile robots can be used in many applications, including exploration in an unknown area. Robots usually carry limited energy so energy conservation is vital. This paper presents an approach for energy-efficient robot exploration. Our approach determines the next target for the robot to visit based upon orientation information. The robot plans the path between the current position to the next target in an energy-efficient way. Our method reduces repeated coverage, a common problem for most existing utility-based target selecting methods. We conduct simulations for both random and structured environments, and compare our method with a utility-based method that chooses the middle cell from the widest opening. Results show that our method can reduce energy consumption by 42% and traveling distance by 41%

Mobile robots can be used in many applications, such as carpet cleaning, search and rescue, and exploration. Many studies have been devoted to the control, sensing, and communication of robots. However, the deployment of robots has not been fully addressed. The deployment problem is to determine the number of groups unloaded by a carrier, the number of robots in each group, and the initial locations of those robots. This paper investigates robot deployment for coverage tasks. Both timing and energy constraints are considered; the robots carry limited energy and need to finish the tasks before deadlines. We build power models for mobile robots and calculate the robots' power consumption at different speeds. A speed-management method is proposed to decide the traveling speeds to maximize the traveling distance under both energy and timing constraints. Our method uses rectangle scanlines as the coverage routes, and solves the deployment problem using fewer robots. Finally, we provide an approach to consider areas with random obstacles. Compared with two simple heuristics, our solution uses 36% fewer robots for open areas and 32% fewer robots for areas with obstacles.

Mobile robots usually carry limited energy and have to accomplish their tasks before deadlines. Examples of these tasks include search and rescue, landmine detection, and carpet cleaning. Many researchers have been studying control, sensing, and coordination for these tasks. However, one major problem has not been fully addressed: the initial deployment of mobile robots. The deployment problem considers the number of robots needed and their initial locations. In this paper, we present a solution for the deployment problem when robots have limited energy and time to collectively accomplish coverage tasks. Simulation results show that our method uses 26% fewer robots comparing with two heuristics for covering the same size of area.

Mobile robots provide new opportunities for research in environment sensing. By combining sensors and robots, we can develop applications to enhance security, search and rescue, and detect hazardous materials. One important problem is to use fewer mobile sensors to cover an area. This paper focuses on two problems in robot sensor deployment: speed management and detouring distance under energy and timing constraints. We determine the robots' traveling speed based on the remaining energy and remaining time before deadline. We use probabilistic models for environments with obstacles and propose an empirical analysis to estimate the extra traveling distance due to detouring. We compute the number of robots (fleet size) needed for cover an area and our approach reduces the fleet size. Compared with two simple heuristics, our method uses 21% fewer robots.

The quickest change detection problem is studied in decentralized decision systems, where a set of sensors receive independent observations and send summary messages to the fusion center, which makes a final decision. In the system where the sensors do not have access to their past observations, the previously conjectured asymptotic optimality of a procedure with a monotone likelihood ratio quantizer (MLRQ) is proved. In the case of additive Gaussian sensor noise, if the signal-to-noise ratios (SNR) at some sensors are sufficiently high, this procedure can perform as well as the optimal centralized procedure that has access to all the sensor observations. Even if all SNRs are low, its detection delay will be at most /spl pi//2-1/spl ap/ 57% larger than that of the optimal centralized procedure. Next, in the system where the sensors have full access to their past observations, the first asymptotically optimal procedure in the literature is developed. Surprisingly, the procedure has the same asymptotic performance as the optimal centralized procedure, although it may perform poorly in some practical situations because of slow asymptotic convergence. Finally, it is shown that neither past message information nor the feedback from the fusion center improves the asymptotic performance in the simplest model.

This paper presents a new approach to find energy-efficient motion plans for mobile robots. Motion planning has two goals: finding the routes and determining the velocities. We model the relationship of motors' speed and their power consumption with polynomials. The velocity of the robot is related to its wheels' velocities by performing a linear transformation. We compare the energy consumption of different routes at different velocities and consider the energy consumed for acceleration and turns. We use experiment-validated simulation to demonstrate up to 51% energy savings for searching an open area.

As robotics technologies improve, mobile robots can be used in many applications. A fundamental question is to decide the number of robots needed (i.e., the "fleet-size problem") to accomplish tasks. Previous studies did not consider the energy constraints of the fleet size problem. In this paper, we present a probabilistic method to decide the fleet size for serving random requests. A simplification method is provided for fast computation. Our method is computationally efficient, with an average of 4% errors validated by an event-driven simulator.

We present the design and implementation of a collaborative intrusion detection system (CIDS) for accurate and efficient intrusion detection in a distributed system. CIDS employs multiple specialized detectors at the different layers - network, kernel and application - and a manager based framework for aggregating the alarms from the different detectors to provide a combined alarm for an intrusion. The premise is that a carefully designed and configured CIDS can increase the accuracy of detection compared to individual detectors, without a substantial degradation in performance. In order to validate the premise, we present the design and implementation of a CIDS which employs Snort, Libsafe, and a new kernel level IDS called Sysmon. The manager has a graph-based and a Bayesian network based aggregation method for combining the alarms to finally come up with a decision about the intrusion. The system is evaluated using a Web-based electronic store front application and under three different classes of attacks - buffer overflow, flooding and script-based attacks. The results show performance degradations compared to no detection of 3.9% and 6.3% under normal workload and a buffer overflow attack respectively. The experiments to evaluate the accuracy of the system show that the normal workload generates false alarms for Snort and the elementary detectors produce missed alarms. CIDS does not flag the false alarm and reduces the incidence of missed alarms to 1 of the 7 cases. CIDS can also be used to measure the propagation time of an intrusion which is useful in choosing an appropriate response strategy.