Introducing spatial information into predictive NF-kappaB modelling--an agent-based approach

Rapid Particle-Based Simulations of Cellular Signalling with the FLAME-Accelerated Signalling Tool (FaST) and GPUs

Cellular signalling is a vital process in living organisms for coordinating highly diverse responses to various stimuli. Particle-based modelling excels in its ability to model complex features of cellular signalling pathways including stochasticity, spatial effects, and heterogeneity, thus improving our understanding of critical decision processes in biology. Yet, particle-based modelling is computationally prohibitive to implement. We recently developed FaST (FLAME-accelerated signalling tool), a software tool that harnesses the power of high-performance computation to reduce the computational burden of particle-based modelling. In particular, employing the unique massively parallel architecture of graphic processing units (GPUs) provided extreme speed ups of simulations by >650-fold. In this chapter, we provide a step-by-step walkthrough of how to use FaST to create GPU-accelerated simulations of a simple cellular signalling network. We further explore how the flexibility of FaST can be used to implement entirely customized simulations while still including the intrinsic speed up advantages of GPU-based parallelization.

Agent-Based Modeling of Complex Molecular Systems

The seamless integration of laboratory experiments and detailed computational modeling provides an exciting route to uncovering many new insights into complex biological processes. In particular, the development of agent-based modeling using supercomputers has provided new opportunities for highly detailed, validated simulations that provide the researcher with greater understanding of these processes and new directions for investigation. This chapter examines some of the principles behind the powerful computational framework FLAME and its application in a number of different areas with a more detailed look at a particular signaling example involving the NF-κB cascade.

Simulating ATO Mechanism and EGFR Signaling with Fuzzy Logic and Petri Net

BACKGROUND

Interactions of many key proteins or genes in signalling pathway have been studied qualitatively in the literature, but only little quantitative information is available.

OBJECTIVE

Although much has been done to clarify the biochemistry of transcriptional dynamics in signalling pathway, it remains difficult to find out and predict quantitative responses. The aim of this study is to construct a computational model of epidermal growth factor receptor (EGFR) signalling pathway as one of hallmarks of cancer so as to predict quantitative responses.

MATERIAL AND METHODS

In this analytical study, we presented a computational model to investigate EGFR signalling pathway. Interaction of Arsenic trioxide (ATO) with EGFR signalling pathway factors has been elicited by systematic search in data bases, as ATO is one of the mysterious chemotherapy agents that control EGFR expression in cancer. ATO has dichotomous manner in vivo, dependent on its concentration. According to fuzzy rules based upon qualitative knowledge and Petri Net, we can construct a quantitative model to describe ATO mechanism in EGFR signalling pathway.

RESULTS

By Fuzzy Logic models that have the potential to trade with the loss of quantitative information on how different species interact, along with Petri net quantitatively describe the dynamics of EGFR signalling pathway. By this model the dynamic of different factors in EGFR signalling pathway is achieved.

CONCLUSION

The use of Fuzzy Logic and PNs in biological network modelling causes a deeper understanding and comprehensive analysis of the biological networks.

Mechanistic dissection of spatial organization in NF-κB signaling pathways by hybrid simulations

The nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) is one of the most important transcription factors involved in the regulation of inflammatory signaling pathways. Inappropriate activation of these pathways has been linked to autoimmunity and cancers. Emerging experimental evidences have been showing the existence of elaborate spatial organizations for various molecular components in the pathways. One example is the scaffold protein tumor necrosis factor receptor associated factor (TRAF). While most TRAF proteins form trimeric quaternary structure through their coiled-coil regions, the N-terminal region of some members in the family can further be dimerized. This dimerization of TRAF trimers can drive them into higher-order clusters as a response to receptor stimulation, which functions as a spatial platform to mediate the downstream poly-ubiquitination. However, the molecular mechanism underlying the TRAF protein clustering and its functional impacts are not well-understood. In this article, we developed a hybrid simulation method to tackle this problem. The assembly of TRAF-based signaling platform at the membrane-proximal region is modeled with spatial resolution, while the dynamics of downstream signaling network, including the negative feedbacks through various signaling inhibitors, is simulated as stochastic chemical reactions. These two algorithms are further synchronized under a multiscale simulation framework. Using this computational model, we illustrated that the formation of TRAF signaling platform can trigger an oscillatory NF-κB response. We further demonstrated that the temporal patterns of downstream signal oscillations are closely regulated by the spatial factors of TRAF clustering, such as the geometry and energy of dimerization between TRAF trimers. In general, our study sheds light on the basic mechanism of NF-κB signaling pathway and highlights the functional importance of spatial regulation within the pathway. The simulation framework also showcases its potential of application to other signaling pathways in cells.

Analysis of mechanotransduction dynamics during combined mechanical stimulation and modulation of the extracellular-regulated kinase cascade uncovers hidden information within the signalling noise

Osteoporosis is a bone disease characterized by brittle bone and increased fracture incidence. With ageing societies worldwide, the disease presents a high burden on health systems. Furthermore, there are limited treatments for osteoporosis with just two anabolic pharmacological agents approved by the US Food and Drug Administration. Healthy bones are believed to be maintained via an intricate relationship between dual biochemical and mechanical (bio-mechanical) stimulations. It is widely considered that osteoporosis emerges as a result of disturbances to said relationship. The mechanotransduction process is key to this balance, and disruption of its dynamics in bone cells plays a role in osteoporosis development. Nonetheless, the exact details and mechanisms that drive and secure the health of bones are still elusive at the cellular and molecular scales. This study examined the dual modulation of mechanical stimulation and mechanotransduction activation dynamics in an osteoblast (OB). The aim was to find patterns of mechanotransduction dynamics demonstrating a significant change that can be mapped to alterations in the OB responses, specifically at the level of gene expression and osteogenic markers such as alkaline phosphatase. This was achieved using a three-dimensional hybrid multiscale computational model simulating mechanotransduction in the OB and its interaction with the extracellular matrix, combined with a numerical analytical technique. The model and the analysis method predict that within the noise of mechanotransduction, owing to modulation of the bio-mechanical stimulus and consequent gene expression, there are unique events that provide signatures for a shift in the system's dynamics. Furthermore, the study uncovered molecular interactions that can be potential drug targets.

Cancer systems immunology

Tumor immunology is undergoing a renaissance due to the recent profound clinical successes of tumor immunotherapy. These advances have coincided with an exponential growth in the development of -omics technologies. Armed with these technologies and their associated computational and modeling toolsets, systems biologists have turned their attention to tumor immunology in an effort to understand the precise nature and consequences of interactions between tumors and the immune system. Such interactions are inherently multivariate, spanning multiple time and size scales, cell types, and organ systems, rendering systems biology approaches particularly amenable to their interrogation. While in its infancy, the field of 'Cancer Systems Immunology' has already influenced our understanding of tumor immunology and immunotherapy. As the field matures, studies will move beyond descriptive characterizations toward functional investigations of the emergent behavior that govern tumor-immune responses. Thus, Cancer Systems Immunology holds incredible promise to advance our ability to fight this disease.

The FLAME-accelerated signalling tool (FaST) for facile parallelisation of flexible agent-based models of cell signalling

Agent-based modelling is particularly adept at modelling complex features of cell signalling pathways, where heterogeneity, stochastic and spatial effects are important, thus increasing our understanding of decision processes in biology in such scenarios. However, agent-based modelling often is computationally prohibitive to implement. Parallel computing, either on central processing units (CPUs) or graphical processing units (GPUs), can provide a means to improve computational feasibility of agent-based applications but generally requires specialist coding knowledge and extensive optimisation. In this paper, we address these challenges through the development and implementation of the FLAME-accelerated signalling tool (FaST), a software that permits easy creation and parallelisation of agent-based models of cell signalling, on CPUs or GPUs. FaST incorporates validated new agent-based methods, for accurate modelling of reaction kinetics and, as proof of concept, successfully converted an ordinary differential equation (ODE) model of apoptosis execution into an agent-based model. We finally parallelised this model through FaST on CPUs and GPUs resulting in an increase in performance of 5.8× (16 CPUs) and 53.9×, respectively. The FaST takes advantage of the communicating X-machine approach used by FLAME and FLAME GPU to allow easy alteration or addition of functionality to parallel applications, but still includes inherent parallelisation optimisation. The FaST, therefore, represents a new and innovative tool to easily create and parallelise bespoke, robust, agent-based models of cell signalling.

Revealing hidden information in osteoblast's mechanotransduction through analysis of time patterns of critical events

BACKGROUND

Mechanotransduction in bone cells plays a pivotal role in osteoblast differentiation and bone remodelling. Mechanotransduction provides the link between modulation of the extracellular matrix by mechanical load and intracellular activity. By controlling the balance between the intracellular and extracellular domains, mechanotransduction determines the optimum functionality of skeletal dynamics. Failure of this relationship was suggested to contribute to bone-related diseases such as osteoporosis.

RESULTS

A hybrid mechanical and agent-based model (Mech-ABM), simulating mechanotransduction in a single osteoblast under external mechanical perturbations, was utilised to simulate and examine modulation of the activation dynamics of molecules within mechanotransduction on the cellular response to mechanical stimulation. The number of molecules and their fluctuations have been analysed in terms of recurrences of critical events. A numerical approach has been developed to invert subordination processes and to extract the direction processes from the molecular signals in order to derive the distribution of recurring events. These predict that there are large fluctuations enclosing information hidden in the noise which is beyond the dynamic variations of molecular baselines. Moreover, studying the system under different mechanical load regimes and altered dynamics of feedback loops, illustrate that the waiting time distributions of each molecule are a signature of the system's state.

CONCLUSIONS

The behaviours of the molecular waiting times change with the changing of mechanical load regimes and altered dynamics of feedback loops, presenting the same variation of patterns for similar interacting molecules and identifying specific alterations for key molecules in mechanotransduction. This methodology could be used to provide a new tool to identify potent molecular candidates to modulate mechanotransduction, hence accelerate drug discovery towards therapeutic targets for bone mass upregulation.

Heterogeneity in The Mechanical Properties of Integrins Determines Mechanotransduction Dynamics in Bone Osteoblasts

Bone cells are exposed to dynamic mechanical stimulation that is transduced into cellular responses by mechanotransduction mechanisms. The extracellular matrix (ECM) provides a physical link between loading and bone cells, where mechanoreceptors, such as integrins, initiate mechanosensation. Though this relationship is well studied, the dynamic interplay between mechanosensation, mechanotransduction and cellular responses is unclear. A hybrid-multiscale model combining molecular, cellular and tissue interactions was developed to examine links between integrins’ mechanosensation and effects on mechanotransduction, ECM modulation and cell-ECM interaction. The model shows that altering integrin mechanosensitivity threshold (MT) increases mechanotransduction durations from hours to beyond 4 days, where bone formation starts. This is relevant to bone, where it is known that a brief stimulating period provides persistent influences for over 24 hours. Furthermore, the model forecasts that integrin heterogeneity, with respect to MT, would be able to induce sustained increase in pERK baseline > 15% beyond 4 days. This is analogous to the emergence of molecular mechanical memory signalling dynamics. Therefore, the model can provide a greater understanding of mechanical adaptation to differential mechanical responses at different times. Given reduction of bone sensitivity to mechanical stimulation with age, these findings may lead towards useful therapeutic targets for upregulation of bone mass.

The IL-1RI Co-Receptor TILRR (FREM1 Isoform 2) Controls Aberrant Inflammatory Responses and Development of Vascular Disease

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The IL-1RI co-receptor, TILRR, is a potent amplifier of IL-1–induced responses.

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Blocking TILRR inhibits IL-1 receptor function and activation of inflammatory genes.

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TILRR expression is high in atherosclerotic lesions but low in healthy tissue, allowing distinct inhibition at sites of inflammation.

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Genetic deletion of TILRR and antibody blocking of TILRR function reduce plaque development and progression of atherosclerosis. Lesions exhibit low levels of macrophages and increased levels of smooth muscle cells and collagen, characteristics of stable plaques.

Expression of the interleukin-1 receptor type I (IL-1RI) co-receptor Toll-like and interleukin-1 receptor regulator (TILRR) is significantly increased in blood monocytes following myocardial infarction and in the atherosclerotic plaque, whereas levels in healthy tissue are low. TILRR association with IL-1RI at these sites causes aberrant activation of inflammatory genes, which underlie progression of cardiovascular disease. The authors show that genetic deletion of TILRR or antibody blocking of TILRR function reduces development of atherosclerotic plaques. Lesions exhibit decreased levels of monocytes, with increases in collagen and smooth muscle cells, characteristic features of stable plaques. The results suggest that TILRR may constitute a rational target for site- and signal-specific inhibition of vascular disease.

Looking "Under the Hood" of Cellular Mechanotransduction with Computational Tools: A Systems Biomechanics Approach across Multiple Scales

Signal modulation has been developed in living cells throughout evolution to promote utilizing the same machinery for multiple cellular functions. Chemical and mechanical modules of signal transmission and transduction are interconnected and necessary for organ development and growth. However, due to the high complexity of the intercommunication of physical intracellular connections with biochemical pathways, there are many missing details in our overall understanding of mechanotransduction processes, i.e., the process by which mechanical signals are converted to biochemical cascades. Cell-matrix adhesions are mechanically coupled to the nucleus through the cytoskeleton. This modulated and tightly integrated network mediates the transmission of mechanochemical signals from the extracellular matrix to the nucleus. Various experimental and computational techniques have been utilized to understand the basic mechanisms of mechanotransduction, yet many aspects have remained elusive. Recently, in silico experiments have made important contributions to the field of mechanobiology. Herein, computational modeling efforts devoted to understanding integrin-mediated mechanotransduction pathways are reviewed, and an outlook is presented for future directions toward using suitable computational approaches and developing novel techniques for addressing important questions in the field of mechanotransduction.

Modelling the molecular mechanisms of aging

The aging process is driven at the cellular level by random molecular damage that slowly accumulates with age. Although cells possess mechanisms to repair or remove damage, they are not 100% efficient and their efficiency declines with age. There are many molecular mechanisms involved and exogenous factors such as stress also contribute to the aging process. The complexity of the aging process has stimulated the use of computational modelling in order to increase our understanding of the system, test hypotheses and make testable predictions. As many different mechanisms are involved, a wide range of models have been developed. This paper gives an overview of the types of models that have been developed, the range of tools used, modelling standards and discusses many specific examples of models that have been grouped according to the main mechanisms that they address. We conclude by discussing the opportunities and challenges for future modelling in this field.

Statistical Techniques Complement UML When Developing Domain Models of Complex Dynamical Biosystems

Computational modelling and simulation is increasingly being used to complement traditional wet-lab techniques when investigating the mechanistic behaviours of complex biological systems. In order to ensure computational models are fit for purpose, it is essential that the abstracted view of biology captured in the computational model, is clearly and unambiguously defined within a conceptual model of the biological domain (a domain model), that acts to accurately represent the biological system and to document the functional requirements for the resultant computational model. We present a domain model of the IL-1 stimulated NF-κB signalling pathway, which unambiguously defines the spatial, temporal and stochastic requirements for our future computational model. Through the development of this model, we observe that, in isolation, UML is not sufficient for the purpose of creating a domain model, and that a number of descriptive and multivariate statistical techniques provide complementary perspectives, in particular when modelling the heterogeneity of dynamics at the single-cell level. We believe this approach of using UML to define the structure and interactions within a complex system, along with statistics to define the stochastic and dynamic nature of complex systems, is crucial for ensuring that conceptual models of complex dynamical biosystems, which are developed using UML, are fit for purpose, and unambiguously define the functional requirements for the resultant computational model.

Reducing complexity in an agent based reaction model-Benefits and limitations of simplifications in relation to run time and system level output

Agent based modelling is a methodology for simulating a variety of systems across a broad spectrum of fields. However, due to the complexity of the systems it is often impossible or impractical to model them at a one to one scale. In this paper we use a simple reaction rate model implemented using the FLAME framework to test the impact of common methods for reducing model complexity such as reducing scale, increasing iteration duration and reducing message overheads. We demonstrate that such approaches can have significant impact on simulation runtime albeit with increasing risk of aberrant system behaviour and errors, as the complexity of the model is reduced.

Regulation of signal transduction by spatial parameters: a case in NF-κB oscillation

NF-κB is a transcription factor regulating expression of more than 500 genes, and its dysfunction leads to the autoimmune and inflammatory diseases. In malignant cancer cells, NF-κB is constitutively activated. Thus the elucidation of mechanisms for NF-κB regulation is important for the establishment of therapeutic treatment caused by incorrect NF-κB responses. Cytoplasmic NF-κB translocates to the nucleus by the application of extracellular stimuli such as cytokines. Nuclear NF-κB is known to oscillate with the cycle of 1.5-4.5 h, and it is thought that the oscillation pattern regulates the expression profiles of genes. In this review, first we briefly describe regulation mechanisms of NF-κB. Next, published computational simulations on the oscillation of NF-κB are summarised. There are at least 60 reports on the computational simulation and analysis of NF-κB oscillation. Third, the importance of a 'space' for the regulation of oscillation pattern of NF-κB is discussed, showing altered oscillation pattern by the change in spatial parameters such as diffusion coefficient, nuclear to cytoplasmic volume ratio (N/C ratio), and transport through nuclear membrane. Finally, simulations in a true intracellular space (TiCS), which is an intracellular 3D space reconstructed in a computer with organelles such as nucleus and mitochondria are discussed.

Computational Modelling of NF-κB Activation by IL-1RI and Its Co-Receptor TILRR, Predicts a Role for Cytoskeletal Sequestration of IκBα in Inflammatory Signalling

The transcription factor NF-κB (nuclear factor kappa B) is activated by Toll-like receptors and controlled by mechanotransduction and changes in the cytoskeleton. In this study we combine 3-D predictive protein modelling and in vitro experiments with in silico simulations to determine the role of the cytoskeleton in regulation of NF-κB. Simulations used a comprehensive agent-based model of the NF-κB pathway, which includes the type 1 IL-1 receptor (IL-1R1) complex and signalling intermediates, as well as cytoskeletal components. Agent based modelling relies on in silico reproductions of systems through the interactions of its components, and provides a reliable tool in investigations of biological processes, which require spatial considerations and involve complex formation and translocation of regulatory components. We show that our model faithfully reproduces the multiple steps comprising the NF-κB pathway, and provides a framework from which we can explore novel aspects of the system. The analysis, using 3-D predictive protein modelling and in vitro assays, demonstrated that the NF-κB inhibitor, IκBα is sequestered to the actin/spectrin complex within the cytoskeleton of the resting cell, and released during IL-1 stimulation, through a process controlled by the IL-1RI co-receptor TILRR (Toll-like and IL-1 receptor regulator). In silico simulations using the agent-based model predict that the cytoskeletal pool of IκBα is released to adjust signal amplification in relation to input levels. The results suggest that the process provides a mechanism for signal calibration and enables efficient, activation-sensitive regulation of NF-κB and inflammatory responses.

Towards a systems level understanding of the oxygen response of Escherichia coli

Escherichia coli is a facultatively anaerobic bacterium. With glucose if no external electron acceptors are available, ATP is produced by substrate level phosphorylation. The intracellular redox balance is maintained by mixed-acid fermentation, that is, the production and excretion of several organic acids. When oxygen is available, E. coli switches to aerobic respiration to achieve redox balance and optimal energy conservation by proton translocation linked to electron transfer. The switch between fermentative and aerobic respiratory growth is driven by extensive changes in gene expression and protein synthesis, resulting in global changes in metabolic fluxes and metabolite concentrations. This oxygen response is determined by the interaction of global and local genetic regulatory mechanisms, as well as by enzymatic regulation. The response is affected by basic physical constraints such as diffusion, thermodynamics and the requirement for a balance of carbon, electrons and energy (predominantly the proton motive force and the ATP pool). A comprehensive systems level understanding of the oxygen response of E. coli requires the integrated interpretation of experimental data that are pertinent to the multiple levels of organization that mediate the response. In the pan-European venture, Systems Biology of Microorganisms (SysMO) and specifically within the project Systems Understanding of Microbial Oxygen Metabolism (SUMO), regulator activities, gene expression, metabolite levels and metabolic flux datasets were obtained using a standardized and reproducible chemostat-based experimental system. These different types and qualities of data were integrated using mathematical models. The approach described here has revealed a much more detailed picture of the aerobic-anaerobic response, especially for the environmentally critical microaerobic range that is located between unlimited oxygen availability and anaerobiosis.

Potential new therapeutic modality revealed through agent-based modeling of the neuromuscular junction and acetylcholinesterase inhibition

Background

One of the leading causes of death and illness within the agriculture industry is through unintentionally ingesting or inhaling organophosphate pesticides. OP intoxication directly inhibits acetylcholinesterase, resulting in an excitatory signaling cascade leading to fasciculation, loss of control of bodily fluids, and seizures.

Methods

Our model was developed using a discrete, rules-based modeling approach in NetLogo. This model includes acetylcholinesterase, the nicotinic acetylcholine receptor responsible for signal transduction, a single release of acetylcholine, organophosphate inhibitors, and a theoretical novel medical countermeasure. We have parameterized the system considering the molecular reaction rate constants in an agent-based approach, as opposed to apparent macroscopic rates used in differential equation models.

Results

Our model demonstrates how the cholinergic crisis can be mitigated by therapeutic intervention with an acetylcholinesterase activator. Our model predicts signal rise rates and half-lives consistent with in vitro and in vivo data in the absence and presence of inhibitors. It also predicts the efficacy of theoretical countermeasures acting through three mechanisms: increasing catalytic turnover of acetylcholine, increasing acetylcholine binding affinity to the enzyme, and decreasing binding rates of inhibitors.

Conclusion

We present a model of the neuromuscular junction confirming observed acetylcholine signaling data and suggesting that developing a countermeasure capable of reducing inhibitor binding, and not activator concentration, is the most important parameter for reducing organophosphate (OP) intoxication.

Electronic supplementary material

The online version of this article (doi:10.1186/1742-4682-11-42) contains supplementary material, which is available to authorized users.

Multi-agent model of hepatitis C virus infection

OBJECTIVES

The objective of this study is to design a method for modeling hepatitis C virus (HCV) infection using multi-agent simulation and to verify it in practice.

METHODS AND MATERIALS

In this paper, first, the modeling of HCV infection using a multi-agent system is compared with the most commonly used model type, which is based on differential equations. Then, the implementation and results of the model using a multi-agent simulation is presented. To find the values of the parameters used in the model, a method using inverted simulation flow and genetic algorithm is proposed. All of the data regarding HCV infection are taken from the paper describing the model based on the differential equation to which the proposed method is compared.

RESULTS

Important advantages of the proposed method are noted and demonstrated: these include flexibility, clarity, re-usability and the possibility to model more complex dependencies. Then, the simulation framework that uses the proposed approach is successfully implemented in C++ and is verified by comparing it to the approach based on differential equations. The verification proves that an objective function that performs the best is the function that minimizes the maximal differences in the data. Finally, an analysis of one of the already known models is performed, and it is proved that it incorrectly models a decay in the hepatocytes number by 40%.

CONCLUSIONS

The proposed method has many advantages in comparison to the currently used model types and can be used successfully for analyzing HCV infection. With almost no modifications, it can also be used for other types of viral infections.

Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity

A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework—a dynamic knowledge repository—wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline. © 2013 Wiley Periodicals, Inc.

Systems biology of complex symptom profiles: capturing interactivity across behavior, brain and immune regulation

As our thinking about the basic principles of biology and medicine continue to evolve, the importance of context and regulatory interaction is becoming increasingly obvious. Biochemical and physiological components do not exist in isolation but instead are part of a tightly integrated network of interacting elements that ensure robustness and support the emergence of complex behavior. This integration permeates all levels of biology from gene regulation, to immune cell signaling, to coordinated patterns of neuronal activity and the resulting psychosocial interaction. Systems biology is an emerging branch of science that sits as a translational catalyst at the interface of the life and computational sciences. While there is no universally accepted definition of systems biology, we attempt to provide an overview of some the basic unifying concepts and current efforts in the field as they apply to illnesses where brain and subsequent behavior are a chief component, for example autism, schizophrenia, depression, and others. Methods in this field currently constitute a broad mosaic that stretches across multiple scales of biology and physiological compartments. While this work by no means constitutes an exhaustive list of all these methods, this work highlights the principal sub-disciplines presently driving the field as well as future directions of progress.

Regulation of NF-κB by ubiquitination and degradation of the IκBs

The nuclear factor-κB (NF-κB) signaling pathway is a busy ground for the action of the ubiquitin-proteasome system; many of the signaling steps are coordinated by protein ubiquitination. The end point of this pathway is to induce transcription, and to this end, there is a need to overcome a major obstacle, a set of inhibitors (IκBs) that bind NF-κB and prohibit either the nuclear entry or the DNA binding of the transcription factor. Two major signaling steps are required for the elimination of the inhibitors: activation of the IκB kinase (IKK) and degradation of the phosphorylated inhibitors. IKK activation and IκB degradation involve different ubiquitination modes; the latter is mediated by a specific E3 ubiquitin ligase SCF(β-TrCP) . The F-box component of this E3, β-TrCP, recognizes the IκB degron formed following phosphorylation by IKK and thus couples IκB phosphorylation to ubiquitination. SCF(β-TrCP) -mediated IκB ubiquitination and degradation is a very efficient process, often resulting in complete degradation of the key inhibitor IκBα within a few minutes of cell stimulation. In vivo ablation of β-TrCP results in accumulation of all the IκBs and complete NF-κB inhibition. As many details of IκB-β-TrCP interaction have been worked out, the development of β-TrCP inhibitors might be a feasible therapeutic approach for NF-κB-associated human disease. However, we may still need to advance our understanding of the mechanism of IκB degradation as well as of the diverse functions of β-TrCP in vivo.

Spatial simulations in systems biology: from molecules to cells

Cells are highly organized objects containing millions of molecules. Each biomolecule has a specific shape in order to interact with others in the complex machinery. Spatial dynamics emerge in this system on length and time scales which can not yet be modeled with full atomic detail. This review gives an overview of methods which can be used to simulate the complete cell at least with molecular detail, especially Brownian dynamics simulations. Such simulations require correct implementation of the diffusion-controlled reaction scheme occurring on this level. Implementations and applications of spatial simulations are presented, and finally it is discussed how the atomic level can be included for instance in multi-scale simulation methods.

Modeling formalisms in Systems Biology

Systems Biology has taken advantage of computational tools and high-throughput experimental data to model several biological processes. These include signaling, gene regulatory, and metabolic networks. However, most of these models are specific to each kind of network. Their interconnection demands a whole-cell modeling framework for a complete understanding of cellular systems. We describe the features required by an integrated framework for modeling, analyzing and simulating biological processes, and review several modeling formalisms that have been used in Systems Biology including Boolean networks, Bayesian networks, Petri nets, process algebras, constraint-based models, differential equations, rule-based models, interacting state machines, cellular automata, and agent-based models. We compare the features provided by different formalisms, and discuss recent approaches in the integration of these formalisms, as well as possible directions for the future.

Modelling complex biological systems using an agent-based approach

Many of the complex systems found in biology are comprised of numerous components, where interactions between individual agents result in the emergence of structures and function, typically in a highly dynamic manner. Often these entities have limited lifetimes but their interactions both with each other and their environment can have profound biological consequences. We will demonstrate how modelling these entities, and their interactions, can lead to a new approach to experimental biology bringing new insights and a deeper understanding of biological systems.

Attenuation of microglial and IL-1 signaling protects mice from acute alcohol-induced sedation and/or motor impairment

Alcohol-induced proinflammatory central immune signaling has been implicated in the chronic neurotoxic actions of alcohol, although little work has examined if these non-neuronal actions contribute to the acute behavioral responses elicited by alcohol administration. The present study examined if acute alcohol-induced sedation (loss of righting reflex, sleep time test) and motor impairment (rotarod test) were influenced by acute alcohol-induced microglial-dependent central immune signaling. Inhibition of acute alcohol-induced central immune signaling, through the reduction of proinflammatory microglial activation with minocycline, or by blocking interleukin-1 (IL-1) receptor signaling using IL-1 receptor antagonist (IL-1ra), reduced acute alcohol-induced sedation in mice. Mice treated with IL-1ra recovered faster from acute alcohol-induced motor impairment than control animals. However, minocycline led to greater motor impairment induced by alcohol, implicating different mechanisms in alcohol-induced sedation and motor impairment. At a cellular level, IκBα protein levels in mixed hippocampal cells responded rapidly to alcohol in a time-dependent manner, and both minocycline and IL-1ra attenuated the elevated levels of IκBα protein by alcohol. Collectively these data suggest that alcohol is capable of rapid modification of proinflammatory immune signaling in the brain and this contributes significantly to the pharmacology of alcohol.

Agent-based simulation of reactions in the crowded and structured intracellular environment: Influence of mobility and location of the reactants

Background

In this paper we apply a novel agent-based simulation method in order to model intracellular reactions in detail. The simulations are performed within a virtual cytoskeleton enriched with further crowding elements, which allows the analysis of molecular crowding effects on intracellular diffusion and reaction rates. The cytoskeleton network leads to a reduction in the mobility of molecules. Molecules can also unspecifically bind to membranes or the cytoskeleton affecting (i) the fraction of unbound molecules in the cytosol and (ii) furthermore reducing the mobility. Binding of molecules to intracellular structures or scaffolds can in turn lead to a microcompartmentalization of the cell. Especially the formation of enzyme complexes promoting metabolic channeling, e.g. in glycolysis, depends on the co-localization of the proteins.

Results

While the co-localization of enzymes leads to faster reaction rates, the reduced mobility decreases the collision rate of reactants, hence reducing the reaction rate, as expected. This effect is most prominent in diffusion limited reactions. Furthermore, anomalous diffusion can occur due to molecular crowding in the cell. In the context of diffusion controlled reactions, anomalous diffusion leads to fractal reaction kinetics. The simulation framework is used to quantify and separate the effects originating from molecular crowding or the reduced mobility of the reactants. We were able to define three factors which describe the effective reaction rate, namely f ^diff for the diffusion effect, f ^volume for the crowding, and f ^access for the reduced accessibility of the molecules.

Conclusions

Molecule distributions, reaction rate constants and structural parameters can be adjusted separately in the simulation allowing a comprehensive study of individual effects in the context of a realistic cell environment. As such, the present simulation can help to bridge the gap between in vivo and in vitro kinetics.

Agent-based models in translational systems biology

Effective translational methodologies for knowledge representation are needed in order to make strides against the constellation of diseases that affect the world today. These diseases are defined by their mechanistic complexity, redundancy, and nonlinearity. Translational systems biology aims to harness the power of computational simulation to streamline drug/device design, simulate clinical trials, and eventually to predict the effects of drugs on individuals. The ability of agent-based modeling to encompass multiple scales of biological process as well as spatial considerations, coupled with an intuitive modeling paradigm, suggests that this modeling framework is well suited for translational systems biology. This review describes agent-based modeling and gives examples of its translational applications in the context of acute inflammation and wound healing.

Ubiquitination and degradation of the inhibitors of NF-kappaB

The key step in NF-kappaB activation is the release of the NF-kappaB dimers from their inhibitory proteins, achieved via proteolysis of the IkappaBs. This irreversible signaling step constitutes a commitment to transcriptional activation. The signal is eventually terminated through nuclear expulsion of NF-kappaB, the outcome of a negative feedback loop based on IkappaBalpha transcription, synthesis, and IkappaBalpha-dependent nuclear export of NF-kappaB (Karin and Ben-Neriah 2000). Here, we review the process of signal-induced IkappaB ubiquitination and degradation by comparing the degradation of several IkappaBs and discussing the characteristics of IkappaBs' ubiquitin machinery.

Agent-based modeling of endotoxin-induced acute inflammatory response in human blood leukocytes

BACKGROUND

Inflammation is a highly complex biological response evoked by many stimuli. A persistent challenge in modeling this dynamic process has been the (nonlinear) nature of the response that precludes the single-variable assumption. Systems-based approaches offer a promising possibility for understanding inflammation in its homeostatic context. In order to study the underlying complexity of the acute inflammatory response, an agent-based framework is developed that models the emerging host response as the outcome of orchestrated interactions associated with intricate signaling cascades and intercellular immune system interactions.

METHODOLOGY/PRINCIPAL FINDINGS

An agent-based modeling (ABM) framework is proposed to study the nonlinear dynamics of acute human inflammation. The model is implemented using NetLogo software. Interacting agents involve either inflammation-specific molecules or cells essential for the propagation of the inflammatory reaction across the system. Spatial orientation of molecule interactions involved in signaling cascades coupled with the cellular heterogeneity are further taken into account. The proposed in silico model is evaluated through its ability to successfully reproduce a self-limited inflammatory response as well as a series of scenarios indicative of the nonlinear dynamics of the response. Such scenarios involve either a persistent (non)infectious response or innate immune tolerance and potentiation effects followed by perturbations in intracellular signaling molecules and cascades.

CONCLUSIONS/SIGNIFICANCE

The ABM framework developed in this study provides insight on the stochastic interactions of the mediators involved in the propagation of endotoxin signaling at the cellular response level. The simulation results are in accordance with our prior research effort associated with the development of deterministic human inflammation models that include transcriptional dynamics, signaling, and physiological components. The hypothetical scenarios explored in this study would potentially improve our understanding of how manipulating the behavior of the molecular species could manifest into emergent behavior of the overall system.

NIK and IKKbeta interdependence in NF-kappaB signalling--flux analysis of regulation through metabolites

Activation of the transcription factor NF-kappaB is central to control of immune and inflammatory responses. Cytokine induced activation through the classical or canonical pathway relies on degradation of the inhibitor, IkappaBalpha and regulation by the IKKbeta kinase. In addition, the NF-kappaB is activated through the NF-kappaB-inducing kinase, NIK. Analysis of the IKK/NIK inter-relationship and its impact on NF-kappaB control, were analysed by mathematical modelling, using matrix formalism and stoichiometrically balanced reactions. The analysis considered a range of bio-reactions and core metabolites and their role in relation to kinase activation and in control of specific steps of the NF-kappaB pathway. The model predicts a growth-rate and time-dependent transfer of the primary kinase activity from IKKbeta to NIK. In addition, it suggests that NIK/IKKbeta interdependence is controlled by intermediates of phosphoribosylpyrophosphate (PRPP) within the glycolysis pathway, and thus, identifies a link between specific metabolic events and kinase activation in inflammatory signal transduction. Subsequent in vitro experiments, carried out to validate the impact of IKK/NIK interdependence, confirmed signal amplification at the level of the NF-kappaB/IkappaBalpha complex control in the presence of both kinases. Further, they demonstrate that the induced potentiation is due to synergistic enhancement of relA-dependent activation.

TILRR, a novel IL-1RI co-receptor, potentiates MyD88 recruitment to control Ras-dependent amplification of NF-kappaB

Host defense against infection is induced by Toll-like and interleukin (IL)-1 receptors, and controlled by the transcription factor NF-κB. Our earlier studies have shown that IL-1 activation impacts cytoskeletal structure and that IL-1 receptor (IL-1RI) function is substrate-dependent. Here we identify a novel regulatory component, TILRR, which amplifies activation of IL-1RI and coordinates IL-1-induced control with mechanotransduction. We show that TILRR is a highly conserved and widely expressed enhancer of IL-1-regulated inflammatory responses and, further, that it is a membrane-bound glycosylated protein with sequence homology to members of the FRAS-1 family. We demonstrate that TILRR is recruited to the IL-1 receptor complex and magnifies signal amplification by increasing receptor expression and ligand binding. In addition, we show that the consequent potentiation of NF-κB is controlled through IL-1RI-associated signaling components in coordination with activation of the Ras GTPase. Using mutagenesis, we demonstrate that TILRR function is dependent on association with its signaling partner and, further, that formation of the TILRR-containing IL-1RI complex imparts enhanced association of the MyD88 adapter during ligand-induced activation of NF-κB. We conclude that TILRR is an IL-1RI co-receptor, which associates with the signaling receptor complex to enhance recruitment of MyD88 and control Ras-dependent amplification of NF-κB and inflammatory responses.

Stochastic simulation of signal transduction: impact of the cellular architecture on diffusion

The transduction of signals depends on the translocation of signaling molecules to specific targets. Undirected diffusion processes play a key role in the bridging of spaces between different cellular compartments. The diffusion of the molecules is, in turn, governed by the intracellular architecture. Molecular crowding and the cytoskeleton decrease macroscopic diffusion. This article shows the use of a stochastic simulation method to study the effects of the cytoskeleton structure on the mobility of macromolecules. Brownian dynamics and single particle tracking were used to simulate the diffusion process of individual molecules through a model cytoskeleton. The resulting average effective diffusion is in line with data obtained in the in vitro and in vivo experiments. It shows that the cytoskeleton structure strongly influences the diffusion of macromolecules. The simulation method used also allows the inclusion of reactions in order to model complete signaling pathways in their spatio-temporal dynamics, taking into account the effects of the cellular architecture.