RA. Fibrin clot structure and fibrinolysis in hypothyroid individuals: the effects of normalising thyroid hormone levels. This issue, pp 1708-10.Many attempts have been made to model the blood coagulation system, as can be seen from a non-exhaustive survey of the literature . It is, indeed, hard to see how such a complicated system could be understood without the help of computer models, especially when not only chemistry but also flow and diffusion have to be taken into account. The question that we want to discuss here is a more restricted one: how far can such modeling, at this moment, be profitably used for diagnostic, pharmacologic and epidemiologic purposes? Thereisnodoubtthatkineticmodelingisawonderfultoolwith which to test a working hypothesis, i.e. to answer the question of what would happen if things are the way that we think they are. A problemarises,however,whenaninsidiousshift ismadefromthe hypothetical to the confident; from Ôwhat would happen if things are the way that I think they areÕ to Ôthings are as I put them in the computer and now I can tell you what happensÕ. Clinicians and epidemiologists are not interested in hypotheses on the mechanismofthrombingeneration;theywanttoknowwhat happensin a patient. Therefore, they are happy with the confident approach and want more of it. The problem is that however sure the author of the program may feel, models in a computer cannot be guaranteed to truthfully represent reality, and always remain contaminated with hypotheses. In biochemical research, this is notaproblem,because,intime,sucherrorswillberecognizedand corrected, and no harm will be done. With patients, we cannot affordmistakeninformation.Theuseofanotquitecorrectmodel will, at a given -and unknown -moment, generate results that do not represent what happens in a patient. Even worse, it can produce wrong results at any moment without the user being aware of it. For medical use, we need precisely that unique model that truthfully represents the patient both qualitatively (what reactions?) and quantitatively (what constants rule these reactions?). It requires exact understanding of how thrombin generation works. The question of how useful computer simulation is for medical application thus reduces to the question of how sure we can be about the information that we put into the computer.It is the gist of our argument that, despite the tremendous increase in our knowledge of the coagulation mechanism, we are not yet able to propose the unique model that corresponds to thrombin generation in a sample of plasma, let alone in a patient.We will develop our argument in two phases. First, we will show that even perfect resemblance between a simulated curve and the outcome of an experiment does not prove that the underlying assumptions can be accepted. We will show that there are infinitely many models that can simulate a given thrombin generation curve, and that computer simulation cannot distinguish between them.As Popper showed us [24], falsification is the name of the scientific game. One black swan d...
The advent of the computer and computer science, and in particular virtual reality, offers new experiment possibilities with numerical simulations and introduces a new type of investigation for the complex systems study : the in virtuo experiment.This work lies on the framework of multi-agent systems. We propose a generic model for systems biology based on reification of the interactions, on a concept of organization and on a multi-model approach. By ``reification'' we understand that interactions are considered as autonomous agents. The aim has been to combine the systemic paradigm and the virtual reality to provide an application able to collect, simulate, experiment and understand the knowledge owned by different biologists working around an interdisciplinary subject. In that case, we have been focused on the urticaria disease understanding.The method permits to integrate different natures of model. We have modeled biochemical reactions, molecular diffusion, cell organisations and mechanical interactions. It also permits to embed different expert system modeling methods like fuzzy cognitive maps.
In the context of biological complex systems multiagent simulation, we present an interaction-agent model for reaction-diffusion problems that enables interaction with the simulation during the execution, and we establish a mathematical validation for our model. We use two types of interaction-agents: on one hand, in a chemical reactor with no spatial dimension -e.g. a cell-, a reaction-agent represents an autonomous chemical reaction between several reactants, and modifies the concentration of reaction products. On the other hand, we use interface-agents in order to take into account the spatial dimension that appears with diffusion : interface-agents achieve the matching transfer of reactants between cells. This approach, where the simulation engine makes agents intervene in a chaotic and asynchronous way, is an alternative to the classical modelwhich is not relevant when the limits conditions are frequently modified-based on partial derivative equations. We enounciate convergence results for our interaction-agent methods, and illustrate our model with an example about coagulation inside a blood vessel.
Abstract. In the context of multi-agent simulation of biological complex systems, we present a reaction-agent model for biological chemical kinetics that enables interaction with the simulation during the execution. In a chemical reactor with no spatial dimension -e.g. a cell-, a reaction-agent represents an autonomous chemical reaction between several reactants : it reads the concentration of reactants, adapts its reaction speed, and modifies consequently the concentration of reaction products. This approach, where the simulation engine makes agents intervene in a chaotic and asynchronous way, is an alternative to the classical model -which is not relevant when the limits conditions change-based on differential systems. We establish formal proofs of convergence for our reaction-agent methods, generally quadratic. We illustrate our model with an example about the extrinsic pathway of blood coagulation.
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