Network models of complex systems

Abstract

This invention describes computer based virtual models of complex systems, together with integrated systems and methods providing a development and execution framework for visual modeling and dynamic simulation of said models. The virtual models can be used for analysis, monitoring, or control of the operation of the complex systems modeled, as well as for information retrieval. More particularly, the virtual models in the present implementation relate to biological complex systems. In the current implementation the virtual models comprise building blocks representing physical, chemical, or biological processes, the pools of entities that participate in those processes, a hierarchy of compartments representing time-intervals or the spatial and / or functional structure of the complex system in which said entities are located and said processes take place, and the description of the composition of those entities. The building blocks encapsulate in different layers the information, data, and mathematical models that characterize and define each virtual model, and a plurality of methods is associated with their components. The models are built by linking instances of the building blocks in a predefined way, which, when integrated by the methods provided in this invention, result in multidimensional networks of pathways. A number of functions and graphical interfaces can be selected for said instances of building blocks, to extract in various forms the information contained in said models. Those functions include: a) on-the-fly creation of displays of interactive multidimensional networks of pathways, according to user selections; b) dynamic quantitative simulations of selected networks; and c) complex predefined queries based on the relative position of pools of entities in the pathways, the role that the pools play in different processes, the location in selected compartments, and / or the structural components of the entities of those pools. The system integrates inferential control with quantitative and scaled simulation methods, and provides a variety of alternatives to deal with complex dynamic systems and with incomplete and constantly evolving information and data.

Description

PARENT CASES [0001] Priority Claimed: This is a divisional application of pending U.S. Ser. No. 08 / 860,975, filed on Dec. 22, 1997, which is a national stage filing under 35 CFR §371 of International Application No. PCT / US96 / 00883, filed on Jan. 17, 1996, which was published in English under PCT Article 21(2) as WO 96 / 22575, and which claims the benefit as a continuation-in-part of the combined U.S. applications Ser. Nos. 08 / 373,688 and 08 / 373,992, both filed Jan. 17, 1995. U.S. Ser. No. 08 / 373,688, now abandoned, was continued as Ser. No. 08 / 889,624, filed Jul. 8, 1997, and issued as U.S. Pat. No. 5,930,154 on Jul. 27, 1999. U.S. Ser. No. 08 / 373,992 issued as U.S. Pat. No. 5,980,096, on Nov. 9, 1999. Related patent EP 0821817, entitled “Control Systems Based on Simulated Virtual Models”, was granted on Jun. 23, 1999. The specifications, drawings and appendixes of each of the above-referenced patent applications are incorporated herein by reference.COMPACT DISC APPENDIX [0002] The T...

AI Technical Summary

Benefits of technology

[0016] Some process industries use large-scale cultivation of microorganisms or mammalian cells, which are extreme cases in terms of complexity when considering those cells as the individual manufacturing plants involved in complex chemical synthesis. Microorganisms are the preferable systems for producing natural substances that have a multitude of uses, such as drugs, foods, additives, biodetergents, biopolymers, and other new and raw materials. Mammalian cells are the preferable systems for producing potent active substances for therapeutic and diagnostic uses. The ultimate level of complexity is using a whole animal as the live factory for continuous production for important secreted proteins. However, the current systems only monitor very general types of phenomena, such as gas pressure, pH, and in some occasions, the concentration of some product that correlates with cell growth or production. For example, for controlling the production of a particular secreted protein that is produced in very low amounts in relation to other proteins, the total protein amount of protein is measured, which is a very poor indicator of how much of the desired protein is produced. Complex mixtures of chemical reactions could be finely controlled externally by modifying the types and amounts of inputs added, if one could predict what will happen by adding those inputs, which requires a good knowledge and a model of such system of reactions. This is particularly the case with biological cellular systems that have very sophisticated methods to transduce the signals provided by ligands in their external environment to the interior of the cell, resulting in the execution of specific functions. Such detailed and accessible mechanistic models of those pathways of reactions are not currently used for monitoring and control systems, but would be highly desirable. This invention provides the system and methods that allows scientists to visually build detailed mechanistic models of the complex systems involved, and to further develop and use inference methods to integrate the simulation of those Virtual Models with inputs from monitoring devices to allow for the intelligent control of the operation of the complex system.

Problems solved by technology

Some process industries use large-scale cultivation of microorganisms or mammalian cells, which are extreme cases in terms of complexity when considering those cells as the individual manufacturing plants involved in complex chemical synthesis.

The ultimate level of complexity is using a whole animal as the live factory for continuous production for important secreted proteins.

However, the current systems only monitor very general types of phenomena, such as gas pressure, pH, and in some occasions, the concentration of some product that correlates with cell growth or production.

For example, for controlling the production of a particular secreted protein that is produced in very low amounts in relation to other proteins, the total protein amount of protein is measured, which is a very poor indicator of how much of the desired protein is produced.

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