Agents in bioinformatics, computational and systems biology

The adoption of agent technologies and multi-agent systems constitutes an emerging area in bioinformatics. In this article, we report on the activity of the Working Group on Agents in Bioinformatics (BIOAGENTS) founded during the first AgentLink III Technical Forum meeting on the 2nd of July, 2004, in Rome. The meeting provided an opportunity for seeding collaborations between the agent and bioinformatics communities to develop a different (agent-based) approach of computational frameworks both for data analysis and management in bioinformatics and for systems modelling and simulation in computational and systems biology. The collaborations gave rise to applications and integrated tools that we summarize and discuss in context of the state of the art in this area. We investigate on future challenges and argue that the field should still be explored from many perspectives ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages to be used by information agents, and to the adoption of agents for computational grids.     

Modeling Imatinib-Treated Chronic Myelogenous Leukemia: Reducing the Complexity of Agent-Based Models

We develop a model for describing the dynamics of imatinib-treated chronic myelogenous leukemia. Our model is based on replacing the recent agent-based model of Roeder et al. (Nat. Med. 12(10):1181–1184, 2006) by a system of deterministic difference equations. These difference equations describe the time-evolution of clusters of individual agents that are grouped by discretizing the state space. Hence, unlike standard agent-base models, the complexity of our model is independent of the number of agents, which allows to conduct simulation studies with a realistic number of cells. This approach also allows to directly evaluate the expected steady states of the system. The results of our numerical simulations show that our model replicates the averaged behavior of the original Roeder model with a significantly reduced computational cost. Our general approach can be used to simplify other similar agent-based models. In particular, due to the reduced computational complexity of our technique, one can use it to conduct sensitivity studies of the parameters in large agent-based systems.     

Agent-based dynamic knowledge representation of Pseudomonas aeruginosa virulence activation in the stressed gut: Towards characterizing host-pathogen interactions in gut-derived sepsis

Background

There is a growing realization that alterations in host-pathogen interactions (HPI) can generate disease phenotypes without pathogen invasion. The gut represents a prime region where such HPI can arise and manifest. Under normal conditions intestinal microbial communities maintain a stable, mutually beneficial ecosystem. However, host stress can lead to changes in environmental conditions that shift the nature of the host-microbe dialogue, resulting in escalation of virulence expression, immune activation and ultimately systemic disease. Effective modulation of these dynamics requires the ability to characterize the complexity of the HPI, and dynamic computational modeling can aid in this task. Agent-based modeling is a computational method that is suited to representing spatially diverse, dynamical systems. We propose that dynamic knowledge representation of gut HPI with agent-based modeling will aid in the investigation of the pathogenesis of gut-derived sepsis.

Methodology/Principal Findings

An agent-based model (ABM) of virulence regulation in Pseudomonas aeruginosa was developed by translating bacterial and host cell sense-and-response mechanisms into behavioral rules for computational agents and integrated into a virtual environment representing the host-microbe interface in the gut. The resulting gut milieu ABM (GMABM) was used to: 1) investigate a potential clinically relevant laboratory experimental condition not yet developed - i.e. non-lethal transient segmental intestinal ischemia, 2) examine the sufficiency of existing hypotheses to explain experimental data - i.e. lethality in a model of major surgical insult and stress, and 3) produce behavior to potentially guide future experimental design - i.e. suggested sample points for a potential laboratory model of non-lethal transient intestinal ischemia. Furthermore, hypotheses were generated to explain certain discrepancies between the behaviors of the GMABM and biological experiments, and new investigatory avenues proposed to test those hypotheses.

Conclusions/Significance

Agent-based modeling can account for the spatio-temporal dynamics of an HPI, and, even when carried out with a relatively high degree of abstraction, can be useful in the investigation of system-level consequences of putative mechanisms operating at the individual agent level. We suggest that an integrated and iterative heuristic relationship between computational modeling and more traditional laboratory and clinical investigations, with a focus on identifying useful and sufficient degrees of abstraction, will enhance the efficiency and translational productivity of biomedical research.     

Engineering entrainment and adaptation in limit cycle systems

Periodic behavior is key to life and is observed in multiple instances and at multiple time scales in our metabolism, our natural environment, and our engineered environment. A natural way of modeling or generating periodic behavior is done by using oscillators, i.e., dynamical systems that exhibit limit cycle behavior. While there is extensive literature on methods to analyze such dynamical systems, much less work has been done on methods to synthesize an oscillator to exhibit some specific desired characteristics. The goal of this article is twofold: (1) to provide a framework for characterizing and designing oscillators and (2) to review how classes of well-known oscillators can be understood and related to this framework. The basis of the framework is to characterize oscillators in terms of their fundamental temporal and spatial behavior and in terms of properties that these two behaviors can be designed to exhibit. This focus on fundamental properties is important because it allows us to systematically compare a large variety of oscillators that might at first sight appear very different from each other. We identify several specifications that are useful for design, such as frequency-locking behavior, phase-locking behavior, and specific output signal shape. We also identify two classes of design methods by which these specifications can be met, namely offline methods and online methods. By relating these specifications to our framework and by presenting several examples of how oscillators have been designed in the literature, this article provides a useful methodology and toolbox for designing oscillators for a wide range of purposes. In particular, the focus on synthesis of limit cycle dynamical systems should be useful both for engineering and for computational modeling of physical or biological phenomena.     

Extending MAM5 Meta-Model and JaCalIV E Framework to Integrate Smart Devices from Real Environments

This paper presents the extension of a meta-model (MAM5) and a framework based on the model (JaCalIVE) for developing intelligent virtual environments. The goal of this extension is to develop augmented mirror worlds that represent a real and virtual world coupled, so that the virtual world not only reflects the real one, but also complements it. A new component called a smart resource artifact, that enables modelling and developing devices to access the real physical world, and a human in the loop agent to place a human in the system have been included in the meta-model and framework. The proposed extension of MAM5 has been tested by simulating a light control system where agents can access both virtual and real sensor/actuators through the smart resources developed. The results show that the use of real environment interactive elements (smart resource artifacts) in agent-based simulations allows to minimize the error between simulated and real system.     

A hybrid agent-based approach for modeling microbiological systems

Models for systems biology commonly adopt Differential Equations or Agent-Based modeling approaches for simulating the processes as a whole. Models based on differential equations presuppose phenomenological intracellular behavioral mechanisms, while models based on Multi-Agent approach often use directly translated, and quantitatively less precise if-then logical rule constructs. We propose an extendible systems model based on a hybrid agent-based approach where biological cells are modeled as individuals (agents) while molecules are represented by quantities. This hybridization in entity representation entails a combined modeling strategy with agent-based behavioral rules and differential equations, thereby balancing the requirements of extendible model granularity with computational tractability. We demonstrate the efficacy of this approach with models of chemotaxis involving an assay of 10³ cells and 1.2×10⁶ molecules. The model produces cell migration patterns that are comparable to laboratory observations.     

Consentaneous Agent-Based and Stochastic Model of the Financial Markets

We are looking for the agent-based treatment of the financial markets considering necessity to build bridges between microscopic, agent based, and macroscopic, phenomenological modeling. The acknowledgment that agent-based modeling framework, which may provide qualitative and quantitative understanding of the financial markets, is very ambiguous emphasizes the exceptional value of well defined analytically tractable agent systems. Herding as one of the behavior peculiarities considered in the behavioral finance is the main property of the agent interactions we deal with in this contribution. Looking for the consentaneous agent-based and macroscopic approach we combine two origins of the noise: exogenous one, related to the information flow, and endogenous one, arising form the complex stochastic dynamics of agents. As a result we propose a three state agent-based herding model of the financial markets. From this agent-based model we derive a set of stochastic differential equations, which describes underlying macroscopic dynamics of agent population and log price in the financial markets. The obtained solution is then subjected to the exogenous noise, which shapes instantaneous return fluctuations. We test both Gaussian and q-Gaussian noise as a source of the short term fluctuations. The resulting model of the return in the financial markets with the same set of parameters reproduces empirical probability and spectral densities of absolute return observed in New York, Warsaw and NASDAQ OMX Vilnius Stock Exchanges. Our result confirms the prevalent idea in behavioral finance that herding interactions may be dominant over agent rationality and contribute towards bubble formation.     

Cellulat: an agent-based intracellular signalling model

The theory of behaviour-based systems (or autonomous agents) constitutes a useful approach for the modelling of intracellular signalling networks. In this sense, a cell can be seen as an adaptive autonomous agent or as a society of such agents, where each can exhibit a particular behaviour depending on its cognitive capabilities. We present an intracellular signalling model obtained by integrating several computational techniques into an agent-based paradigm. Cellulat, the model, takes into account two essential aspects of the intracellular signalling networks: (1) cognitive capacities, which are modelled as the agent abilities to interact with the surrounding medium and (2) a spatial organisation, this last obtained using a shared data structure through which the agents communicate between them. We propose a methodology for the modelling of intracellular signalling pathway using Cellulat and we discuss the goal of a virtual laboratory based on our model and presently under development.     

基于智能体模型的青岛市林地生态格局评价与优化

设计并在GIS平台上开发了基于智能体的生态格局评价模型,以青岛市及周边地区林地为研究对象,分析不同林地空间格局及生态网络保护框架对于物种生存与扩散的影响.结果表明,与现状相比,不同等级的生态网络框架对物种种群数量与物种迁移都有明显提升,且等级越高的生态网络框架提升作用越明显.然而仅仅依靠生态网络框架不足以使研究区域林地系统形成功能上的相互连通,因此,在分析研究区域现状土地利用格局基础上,提出与湿地系统结合,在胶州湾周围及大沽河干流地区增加林地的空间布局.通过模型模拟分析,发现优化后的林地空间格局结合生态网络框架能有效提升林地之间的物种扩散.基于模拟结果,为研究区林地生态格局构建提出如下建议:(1)保证现有的规模较大的林地不被破坏;(2)青岛市中部湿地系统可以作为新增林地的理想区域;(3)生态网络框架可作为青岛市建立城市组团间生态间隔的空间参考.     

Imitative Learning as a Connector of Collective Brains

The notion that cooperation can aid a group of agents to solve problems more efficiently than if those agents worked in isolation is prevalent in computer science and business circles. Here we consider a primordial form of cooperation – imitative learning – that allows an effective exchange of information between agents, which are viewed as the processing units of a social intelligence system or collective brain. In particular, we use agent-based simulations to study the performance of a group of agents in solving a cryptarithmetic problem. An agent can either perform local random moves to explore the solution space of the problem or imitate a model agent – the best performing agent in its influence network. There is a trade-off between the number of agents and the imitation probability , and for the optimal balance between these parameters we observe a thirtyfold diminution in the computational cost to find the solution of the cryptarithmetic problem as compared with the independent search. If those parameters are chosen far from the optimal setting, however, then imitative learning can impair greatly the performance of the group.     

Mechanobiological model of arterial growth and remodeling

A coupled agent-based model (ABM) and finite element analysis (FEA) computational framework is developed to study the interplay of bio-chemo-mechanical factors in blood vessels and their role in maintaining homeostasis. The agent-based model implements the power of REPAST Simphony libraries and adapts its environment for biological simulations. Coupling a continuum-level model (FEA) to a cellular-level model (ABM) has enabled this computational framework to capture the response of blood vessels to increased or decreased levels of growth factors, proteases and other signaling molecules (on the micro scale) as well as altered blood pressure. Performance of the model is assessed by simulating porcine left anterior descending artery under normotensive conditions and transient increases in blood pressure and by analyzing sensitivity of the model to variations in the rule parameters of the ABM. These simulations proved that the model is stable under normotensive conditions and can recover from transient increases in blood pressure. Sensitivity studies revealed that the model is most sensitive to variations in the concentration of growth factors that affect cellular proliferation and regulate extracellular matrix composition (mainly collagen).     

Neuromorphic hardware databases for exploring structure-function relationships in the brain.

Neuromorphic hardware is the term used to describe full custom-designed integrated circuits, or silicon ''chips'', that are the product of neuromorphic engineering--a methodology for the synthesis of biologically inspired elements and systems, such as individual neurons, retinae, cochleas, oculomotor systems and central pattern generators. We focus on the implementation of neurons and networks of neurons, designed to illuminate structure-function relationships. Neuromorphic hardware can be constructed with either digital or analogue circuitry or with mixed-signal circuitry--a hybrid of the two. Currently, most examples of this type of hardware are constructed using analogue circuits, in complementary metal-oxide-semiconductor technology. The correspondence between these circuits and neurons, or networks of neurons, can exist at a number of levels. At the lowest level, this correspondence is between membrane ion channels and field-effect transistors. At higher levels, the correspondence is between whole conductances and firing behaviour, and filters and amplifiers, devices found in conventional integrated circuit design. Similarly, neuromorphic engineers can choose to design Hodgkin-Huxley model neurons, or reduced models, such as integrate-and-fire neurons. In addition to the choice of level, there is also choice within the design technique itself; for example, resistive and capacitive properties of the neuronal membrane can be constructed with extrinsic devices, or using the intrinsic properties of the materials from which the transistors themselves are composed. So, silicon neurons can be built, with dendritic, somatic and axonal structures, and endowed with ionic, synaptic and morphological properties. Examples of the structure-function relationships already explored using neuromorphic hardware include correlation detection and direction selectivity. Establishing a database for this hardware is valuable for two reasons: first, independently of neuroscientific motivations, the field of neuromorphic engineering would benefit greatly from a resource in which circuit designs could be stored in a form appropriate for reuse and re-fabrication. Analogue designers would benefit particularly from such a database, as there are no equivalents to the algorithmic design methods available to designers of digital circuits. Second, and more importantly for the purpose of this theme issue, is the possibility of a database of silicon neuron designs replicating specific neuronal types and morphologies. In the future, it may be possible to use an automated process to translate morphometric data directly into circuit design compatible formats. The question that needs to be addressed is: what could a neuromorphic hardware database contribute to the wider neuroscientific community that a conventional database could not? One answer is that neuromorphic hardware is expected to provide analogue sensory-motor systems for interfacing the computational power of symbolic, digital systems with the external, analogue environment. It is also expected to contribute to ongoing work in neural-silicon interfaces and prosthetics. Finally, there is a possibility that the use of evolving circuits, using reconfigurable hardware and genetic algorithms, will create an explosion in the number of designs available to the neuroscience community. All this creates the need for a database to be established, and it would be advantageous to set about this while the field is relatively young. This paper outlines a framework for the construction of a neuromorphic hardware database, for use in the biological exploration of structure-function relationships.     

Generic framework for high-dimensional fixed-effects ANOVA

In functional genomics it is more rule than exception that experimental designs are used to generate the data. The samples of the resulting data sets are thus organized according to this design and for each sample many biochemical compounds are measured, e.g. typically thousands of gene-expressions or hundreds of metabolites. This results in high-dimensional data sets with an underlying experimental design. Several methods have recently become available for analyzing such data while utilizing the underlying design. We review these methods by putting them in a unifying and general framework to facilitate understanding the (dis-)similarities between the methods. The biological question dictates which method to use and the framework allows for building new methods to accommodate a range of such biological questions. The framework is built on well known fixed-effect ANOVA models and subsequent dimension reduction. We present the framework both in matrix algebra as well as in more insightful geometrical terms. We show the workings of the different special cases of our framework with a real-life metabolomics example from nutritional research and a gene-expression example from the field of virology.     

A probabilistic meta-predictor for the MHC class II binding peptides

Several computational methods for the prediction of major histocompatibility complex (MHC) class II binding peptides embodying different strengths and weaknesses have been developed. To provide reliable prediction, it is important to design a system that enables the integration of outcomes from various predictors. The construction of a meta-predictor of this type based on a probabilistic approach is introduced in this paper. The design permits the easy incorporation of results obtained from any number of individual predictors. It is demonstrated that this integrated method outperforms six state-of-the-art individual predictors based on computational studies using MHC class II peptides from 13 HLA alleles and three mouse MHC alleles obtained from the Immune Epitope Database and Analysis Resource. It is concluded that this integrative approach provides a clearly enhanced reliability of prediction. Moreover, this computational framework can be directly extended to MHC class I binding predictions.     

城市再生视野下高密度城区生态空间规划方法——以厦门本岛立体绿化专项规划为例

面对快速城市化进程中城市高密度地区生态空间缺乏、布局支离破碎的问题,从规划层面分析了其问题的症结所在;提出屋顶绿化与城市绿地系统联动互补,是针对城市高密度地区生态环境受损严重以及土地资源紧缺的约束条件下,建构生态空间网络的有效途径;并以厦门本岛为研究区,基于GIS、遥感技术,利用高清遥感数据结合实地调研、问卷调查、部门资料、规划文本建立现状及规划空间数据库;从既有研究梳理、综合效益影响、城市发展诉求分析3个层面总结屋顶绿化实施潜力影响因子,结合规划分区与建筑分类建立实施潜力评估方法;对研究区进行屋顶绿化实施潜力评估,结合城市绿地系统,以"全域空间、重点片区、多维网络"为方法进行研究区生态空间网络规划。结果表明:1)生态结构完善、城市功能优化、公共服务需求、环境改善需求、建筑属性特征、建筑产权归属是屋顶绿化实施潜力评估要素,其包含的城市生态框架、城市功能结构、景观提升需求、建筑改造需求、内涝程度、热岛强度、微气候环境、建筑高度、建筑年代、建筑类型、建筑产权属性是关键性评估因子;2)厦门本岛2016年屋顶绿化率仅有2%,且附属于居住建筑和城中村的屋顶绿化量超过总量的一半,商业、公共、产业建筑作为空间载体进行屋顶绿化有较大发展空间,未绿化的屋顶资源中有81.3%适宜进行屋顶绿化;3)探索高密度城区生态空间网络布局的规划方法是本研究的第一步,下一步通过借助AI、GIS等技术,尝试防控高强度开发与高危集聚引发的城市生态和灾害问题,构建满足复合功能的生态空间网络优化的研究方法。     

Experimental Evaluation of Suitability of Selected Multi-Criteria Decision-Making Methods for Large-Scale Agent-Based Simulations

Multi-criteria decision-making (MCDM) can be formally implemented by various methods. This study compares suitability of four selected MCDM methods, namely WPM, TOPSIS, VIKOR, and PROMETHEE, for future applications in agent-based computational economic (ACE) models of larger scale (i.e., over 10 000 agents in one geographical region). These four MCDM methods were selected according to their appropriateness for computational processing in ACE applications. Tests of the selected methods were conducted on four hardware configurations. For each method, 100 tests were performed, which represented one testing iteration. With four testing iterations conducted on each hardware setting and separated testing of all configurations with the–server parameter de/activated, altogether, 12800 data points were collected and consequently analyzed. An illustrational decision-making scenario was used which allows the mutual comparison of all of the selected decision making methods. Our test results suggest that although all methods are convenient and can be used in practice, the VIKOR method accomplished the tests with the best results and thus can be recommended as the most suitable for simulations of large-scale agent-based models.     

A life-like virtual cell membrane using discrete automata

A framework is presented that captures the discrete and probabilistic nature of molecular transport and reaction kinetics found in a living cell as well as formally representing the spatial distribution of these phenomena. This particle or agent-based approach is computationally robust and complements established methods. Namely it provides a higher level of spatial resolution than formulations based on ordinary differential equations (ODE) while offering significant advantages in computational efficiency over molecular dynamics (MD). Using this framework, a model cell membrane has been constructed with discrete particle agents that respond to local component interactions that resemble flocking or herding behavioural cues in animals. Results from simulation experiments are presented where this model cell exhibits many of the characteristic behaviours associated with its biological counterpart such as lateral diffusion, response to osmotic pressure gradients, membrane growth and cell division. Lateral diffusion rates and estimates for the membrane modulus of elasticity derived from these simple experiments fall well within a biologically relevant range of values. More importantly, these estimates were obtained by applying a simple qualitative tuning of the model membrane. Membrane growth was simulated by injecting precursor molecules into the proto-cell at different rates and produced a variety of morphologies ranging from a single large cell to a cluster of cells. The computational scalability of this methodology has been tested and results from benchmarking experiments indicate that real-time simulation of a complete bacterial cell will be possible within 10 years.     

An integrated agent-mathematical model of the effect of intercellular signalling via the epidermal growth factor receptor on cell proliferation

We have previously developed Epitheliome, a software agent representation of the growth and repair characteristics of epithelial cell populations, where cell behaviour is governed by a number of simple rules. In this paper, we describe how this model has been extended to incorporate an example of a molecular 'mechanism' behind a rule-in this case, how signalling by both endogenous and exogenous ligands of the epidermal growth factor receptor (EGFR) can impact on the proliferation of cell agents. We have developed a mathematical model representing release of endogenous ligand by cells, three-dimensional diffusion of the secreted molecules through a volume of cell culture medium, ligand-receptor binding, and bound receptor internalization and trafficking. Information relating to quantities of molecular species associated with each cell agent is frequently exchanged between the agent and signalling models, and the ratio of bound to free receptors determines cell cycle progression and hence the proliferative behaviour of the cell agents. We have applied this integrated model to examine the effect of plating density on tissue growth via autocrine/paracrine signalling. This predicts that cell growth is dependent on the concentration of exogenous ligand, but where this is limited, then growth becomes dependent on cell density and the availability of endogenous ligand. We have further modified the calcium concentration of the medium to modulate the formation of intercellular bonds between cells and shown that the increased propensity for cells to form colonies in physiological calcium does not result in significantly different patterns of receptor occupancy. In conclusion, our approach demonstrates that by combining agent-based and mathematical modelling paradigms, it is possible to probe the complex feedback relationship between the behaviour of individual cells and their interaction with one another and their environment.     

AgentCell: a digital single-cell assay for bacterial chemotaxis

MOTIVATION: In recent years, single-cell biology has focused on the relationship between the stochastic nature of molecular interactions and variability of cellular behavior. To describe this relationship, it is necessary to develop new computational approaches at the single-cell level. RESULTS: We have developed AgentCell, a model using agent-based technology to study the relationship between stochastic intracellular processes and behavior of individual cells. As a test-bed for our approach we use bacterial chemotaxis, one of the best characterized biological systems. In this model, each bacterium is an agent equipped with its own chemotaxis network, motors and flagella. Swimming cells are free to move in a 3D environment. Digital chemotaxis assays reproduce experimental data obtained from both single cells and bacterial populations.