Network models of biochemical pathways

Abstract

This invention describes computer based systems and methods for modeling and simulation of biochemical networks of pathways, including metabolic, signal transduction and regulatory pathways within a cell or across cells. The invention comprises systems and methods for building the models and for using the models for analysis and information retrieval, for determining the effect that modulating one or more reactions in a biochemical pathway has on an operation of the biochemical pathway, and for simulating or predicting an altered physiological state of cells.

Description

PRIORITY CLAIMED [0001] 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. application 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 patent U.S. Pat. No. 5,930,154 on Jul. 27, 1999. U.S. Ser. No. 08 / 373,992 issued as patent 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

[0014] 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.

[0020] The compartmentalized bioModels, and the methods attached to them, encode knowledge that enables the program to reason about the containment of different parts of the model in several compartments, while the architecture of the network of linked bioObjects of diverse types is transparently maintained, regardless of the transfer of the bioObject icons to different locations. They also comprise quantitative variables and parameters, some relating to quantities and rates translocation, distributed either within the corresponding reservoirs and process or relating to time intervals that may be associated with the time compartments, all of which have associated simulation formulas to compute their values. In addition, the modeler can define expert rules to monitor the values of any of those variables while the simulation is running, and to either set other values or control the course of the simulation in a variety of ways. The expert rules can also reason about time or about events resulting from the simulation, and Inference using those types of knowledge may direct further actions to be executed by the 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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