Investigating biocomplexity through the agent-based paradigm.

Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines--or agents--to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex

Dealing with diversity in computational cancer modeling.

This paper discusses the need for interconnecting computational cancer models from different sources and scales within clinically relevant scenarios to increase the accuracy of the models and speed up their clinical adaptation, validation, and eventual translation. We briefly review current interoperability efforts drawing upon our experiences with the development of in silico models for predictive oncology within a number of European Commission Virtual Physiological Human initiative projects on cancer. A clinically relevant scenario, addressing brain tumor modeling that illustrates the need for coupling models from different sources and levels of complexity, is described. General approaches to enabling interoperability using XML-based markup languages for biological modeling are reviewed, concluding with a discussion on efforts towards developing cancer-specific XML markup to couple multiple component models for predictive in silico oncology

Multi-agent CHANS: BDI Farmer Intentions and Decision Making

This paper extends previous works on multi-agent-based simulation models of Coupled Human and Natural Systems (CHANS), by introducing a farmer agent model capable of interact with environmental, economic, and spatial variables in the context of supply and demand of environmental services. Emphasis is made on how the Farmer Agent implements the BDI framework (Believes, Desires, and Intentions) at its core. Also, insights about its decision-making mechanism based on fuzzy logic are provided. Preliminary results are shown in terms of modulating variables such as knowledge, money, well-being, energy, and productivity

Degeneracy: a link between evolvability, robustness and complexity in biological systems

A full accounting of biological robustness remains elusive; both in terms of the mechanisms by which robustness is achieved and the forces that have caused robustness to grow over evolutionary time. Although its importance to topics such as ecosystem services and resilience is well recognized, the broader relationship between robustness and evolution is only starting to be fully appreciated. A renewed interest in this relationship has been prompted by evidence that mutational robustness can play a positive role in the discovery of adaptive innovations (evolvability) and evidence of an intimate relationship between robustness and complexity in biology. This paper offers a new perspective on the mechanics of evolution and the origins of complexity, robustness, and evolvability. Here we explore the hypothesis that degeneracy, a partial overlap in the functioning of multi-functional components, plays a central role in the evolution and robustness of complex forms. In support of this hypothesis, we present evidence that degeneracy is a fundamental source of robustness, it is intimately tied to multi-scaled complexity, and it establishes conditions that are necessary for system evolvability

Exploiting Clinical Trial Data Drastically Narrows the Window of Possible Solutions to the Problem of Clinical Adaptation of a Multiscale Cancer Model

The development of computational models for simulating tumor growth and response to treatment has gained significant momentum during the last few decades. At the dawn of the era of personalized medicine, providing insight into complex mechanisms involved in cancer and contributing to patient-specific therapy optimization constitute particularly inspiring pursuits. The in silico oncology community is facing the great challenge of effectively translating simulation models into clinical practice, which presupposes a thorough sensitivity analysis, adaptation and validation process based on real clinical data. In this paper, the behavior of a clinically-oriented, multiscale model of solid tumor response to chemotherapy is investigated, using the paradigm of nephroblastoma response to preoperative chemotherapy in the context of the SIOP/GPOH clinical trial. A sorting of the model's parameters according to the magnitude of their effect on the output has unveiled the relative importance of the corresponding biological mechanisms; major impact on the result of therapy is credited to the oxygenation and nutrient availability status of the tumor and the balance between the symmetric and asymmetric modes of stem cell division. The effect of a number of parameter combinations on the extent of chemotherapy-induced tumor shrinkage and on the tumor's growth rate are discussed. A real clinical case of nephroblastoma has served as a proof of principle study case, demonstrating the basics of an ongoing clinical adaptation and validation process. By using clinical data in conjunction with plausible values of model parameters, an excellent fit of the model to the available medical data of the selected nephroblastoma case has been achieved, in terms of both volume reduction and histological constitution of the tumor. In this context, the exploitation of multiscale clinical data drastically narrows the window of possible solutions to the clinical adaptation problem

(WP 2023-02) What Are Reflexive Economic Agents? Position-Adjustment, SLAM, and Self-Organization

If mainstream economics and its view of economic agents is designed for a world in which reflexivity and feedback processes in the economy are ‘tamed’ and predictable, how are we to understand economic agents in a world in which reflexivity is ‘untamed’ and economies regularly exhibit unexpected fluctuations and significant nonlinearities? In a nonlinear world, economies evolve and undergo critical phase transitions from one form of organization to another. It seems, then, that we should also expect economic agents to evolve and undergo critical phase transitions from being one type of agent to another just as we observe that economies evolve and undergo phase transitions from being one type of economy to another. Minsky’s analysis of how economies evolve in financial crises and how firms as agents evolve as their financial status changes seems a clear example of this. But then we would need a new conception of what economic agents are. This chapter offers such a conception in the idea of reflexive economic agents, both to redevelop an evolutionary, complexity account of what agents must be and also to forestall complexity researchers from falling back upon the standard utility conception of individuals. The chapter builds its reflexive agents conception around Herbert Simon’s complexity thinking about quasi-independence. It describes reflexive economic agents in what it call position-adjustment terms, and focusing on the ‘reflexive moment’ when agents find they need to revise and adjust their positions in regard to what they are doing. To explain how we can understand adjustment, the chapter employs the thinking behind recent ‘simultaneous localization and mapping’ (SLAM) research in robotics engineering to explain how agents understood in position-adjustment terms can be attributed a form of mobility understood as a capacity for self-direction reliant on a kind of locational self-awareness. The chapter then frames the reflexive individual conception that results in terms of Simon’s quasi-independence, evaluates this conception in identity terms, and then returns to the issue of why complex economic systems made up of utility maximizing agents cannot function as evolutionary systems. The chapter closes with a discussion of complex systems seen to evolve through phase transitions

Agent-based methodology for developing agroecosystems simulations

The agent-based modeling (ABM) approach allows modeling complex systems, involving different kinds of interacting autonomous agents with heterogeneous behavior. Agro-ecosystems (ecological systems subject to human interaction) are a kind of complex system whose analysis and simulation is of interest to several disciplines (e.g. agronomy, ecology or sociology). In this context, the ABM approach appears as a suitable tool for modeling agro-ecosystems, along with a corresponding agent-oriented software engineering (AOSE) methodology for the construction of the simulation. Nevertheless, existing AOSE methodologies are general-purpose, they have not yet accomplished widespread use, and clear examples of applications to agro-ecosystems are hard to find. This thesis sets the ground for a new software development methodology for developing agro-ecosystem simulations based on the ABM approach as well as on these already existing AOSE methodologies, but tailored to tackle specific agro-ecosystem features.El enfoque de modelado basado en agentes (ABM) permite el modelado de sistemas complejos en los que interactúan diferentes tipos de agentes autónomos con comportamientos heterogéneos. Los agro-ecosistemas (sistemas ecológicos sujetos a la presencia humana) son un tipo de sistema complejo cuyo análisis y simulación resulta de interés para diversas disciplinas (ej.: agronomía, ecología o sociología). En este contexto, el enfoque ABM aparece como una herramienta adecuada para el modelado de agro-ecosistemas, junto con una correspondiente metodología de desarrollo de software también orientada a agentes (AOSE) para la construcción de dicha simulación. Si bien ya existen metodologías AOSE, éstas son de propósito general, no han logrado un amplio uso y ejemplos claros de aplicaciones a agro-ecosistemas son difíciles de encontrar. Esta tesis establece los fundamentos para crear una nueva metodología de desarrollo de software basada en el enfoque de agentes para el desarrollo de simulaciones de agro-ecosistemas, basándose en las metodologías AOSE ya existentes, pero personalizada para soportar las características específicas de los agro-ecosistemas