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.

142 . A method of determining in a computer system an effect that modulating one or more reactions in a biochemical pathway has on an operation of the biochemical pathway, comprising a) generating first representation of a biochemical pathway by dynamically determining substances and processes that form the biochemical pathway, and an order in which the substances appear and the processes occur in the biochemical pathway; b) generating a second representation of the biochemical pathway, wherein a definition of at least one substance or process of the first biochemical pathway is changed so as to modulate at least one reaction of the first biochemical pathway; and c) comparing the first and second representations of the biochemical pathway and determining an effect of modulating the at least one reaction of the biochemical pathway.  
   
   
       143 . (canceled)  
   
   
       149 - 154 . (canceled)  
   
   
       161 . A computer readable medium or media, comprising: a) a model relating a plurality of reactants to a plurality of reactions representing a biochemical reaction network, wherein a plurality of said reactions comprises a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said substrate and said product, and wherein at least one of said reactions is a regulated reaction; b) a constraint set for said plurality of reactions, wherein said constraint set comprises a variable constraint for said regulated reaction, wherein said constraint set when applied to said plurality of reactions by program instructions in a computer system results in a model of said biochemical reaction network.  
   
   
       162 . The computer readable medium or media of  claim 161 , further comprising a regulatory data structure representing an event that regulates the biochemical reaction network, wherein said variable constraint is dependent upon, an outcome of said regulatory data structure.  
   
   
       163 . The computer readable medium or media of  claim 162 , wherein said biochemical reaction network represents reactions that occur in a first cell in a population of cells and said regulatory data structure represents one or more events that occur in a second cell in said population.  
   
   
       164 . The computer readable medium or media of  claim 162 , further comprising a user interface capable of sending at least one program instruction for modifying said data structure, said constraint set or said program instructions for applying said constraint set to said data representation, or a combination thereof.  
   
   
       165 . (canceled)  
   
   
       166 . The computer readable medium or media of  claim 161 , wherein a first substrate duct in said plurality of reactions is assigned to a first compartment and a second substrate duct in said plurality of reactions is assigned to a second compartment.  
   
   
       167 - 191 . (canceled)  
   
   
       211 - 215 . (canceled)  
   
   
       218 - 219 . (canceled)  
   
   
       221 . A method of predicting an altered physiological state of a cell comprising the steps of: a) specifying a biochemical network of a cell; b) simulating said network by specifying the components of said network, and representing interrelationships between said components in one or more mathematical equations and setting the quantitative parameters of said components; c) constraining the values of the parameters of said components to represent a first state of the cell; d) perturbing the simulated network by adding or deleting one or more components thereof, changing the concentration of one or more components thereof or modifying one or more mathematical equations representing interrelationships between one or more of said components; e) solving the equations representing the perturbed network to simulate a second state of the cell; and f) comparing said first and second simulated states of the network to identify the effect of said perturbation on the state of the cell.  
   
   
       222 . The method of  claim 221  including the steps of storing said mathematical equations in computer memory, storing algorithms in computer memory for solving said mathematical equations, said solving step or steps each comprising retrieving said algorithms and applying them to solve said equations.  
   
   
       225 - 227 . (canceled)  
   
   
       234 - 235 . (canceled)  
   
   
       240 - 246 . (canceled)  
   
   
       247 - 248 . (canceled)  
   
   
       249 - 250 . (canceled)  
   
   
       251 - 252 . (canceled)  
   
   
       259 . A method of predicting a behavior of a biochemical system, comprising: a) obtaining a first data integration map of a biochemical system, said data integration map comprising value sets of two or more data elements for at least one network; b) producing a second data integration map of said biochemical system under a perturbed condition, said second data integration map comprising perturbed value sets of two or more data elements for said at least one network, and c) identifying correlative changes in at least two value sets in said second data integration map with said perturbed condition, wherein said correlative changes predict a behavior of said biochemical system.  
   
   
       260 . The method of  claim 259 , wherein said biochemical system is selected from the group consisting of a cell, tissue and organism, or a constituent system thereof.  
   
   
       261 . The method of  claim 259 , wherein said second data integration network further comprises two or more perturbed conditions.  
   
   
       262 . The method of  claim 259 , wherein said behavior is selected from the group consisting of cellular phenotype, biochemical activity, expression level and accumulation level.  
   
   
       263 - 264 . (canceled)  
   
   
       270 . A system for constructing a scalable output network model of a biological system or subsystem, comprising: a) an input data set of network components comprising a set of components representing genes and/or gene products; b) executable instructions forming a data structure associating said network components with network process/reaction components, said data structure establishing a data set specifying a network model of connectivity and direction of flow of said network components, wherein said toplogical description defines a scalable output network model of the complex biological system or subsystem.  
   
   
       271 . The system of  claim 270  wherein said output network model is a scalable phenotypic output network model of a living organism or component, further comprising executable instructions modifying said data set to fit a set of desired constraints on said specified network model, wherein said set of desired constraints define a phenotypic output of said network model of a living organism or component.  
   
   
       394 . A computer-implemented method of predicting biological pathways comprising: 
 (a) modeling components of signal cascades of pathways that occur when stimuli are introduced;    (b) dynamically generating results using a simulation module, the simulation module comprising an inference engine linked to at least one dynamic database, the at least one dynamic database containing definitions relating to cellular concepts, components and reactions.    
   
   
       395 . The computer-implemented method of  claim 394  wherein said definitions of concepts, components and reactions are organized in a hierachical ontology  
   
   
       396 . The computer-implemented method of  claim 394  wherein instances of said concepts, components and reactions are organized in a modular hierarchy.  
   
   
       397 . The computer-implemented method of claim  394 c wherein said modules in the hierarchy represent functional, locational or temporal compartments, or any combination thereof.  
   
   
       398 . The method of  claim 142  wherein said generating a first representation of a biochemical pathway further comprises dynamically selecting the substances and/or processes of said biochemical pathway from a database of substances and/or processes.  
   
   
       399 . The method of  claim 142  wherein said representations of the biochemical pathway further comprise quantitative variables, further comprising applying algorithms for solving mathematical equations over said variables to quantitatively determining an effect of modulating the at least one reaction of the biochemical pathway  
   
   
       400 . The method of  claim 142  wherein said biochemical pathway spans two more cellular compartments, cells, organs, physiological systems or organisms.  
   
   
       401 . The method of  claim 142  applied to identifying a potential pharmacological target in the biochemical pathway that affects a physiological or pathological condition of an organism, further comprising determining a substance participating in a reaction or a process which, when modulated, alters the biochemical pathway in a desired manner, wherein the substance or process is a potential target for a drug.  
   
   
       402 . The method of  claim 142  applied to identifying a potential pharmacological agent that act upon the biochemical pathway to affects a physiological or pathological condition of an organism, further comprising determining an agent which modulate one or more process or substance participating in a reaction to alter the biochemical pathway in a desired manner, wherein the agent is a potential drug.  
   
   
       403 . The computer readable medium or media of  claim 161 , further comprising program instructions to apply said constraint set to said plurality of reactions in a computer system to generate a model of said biochemical reaction network.  
   
   
       404 . The computer readable medium or media of  claim 161 , wherein said biochemical reaction network further comprises regulatory reactions.  
   
   
       405 . The computer readable medium or media of  claim 161 , wherein said biochemical reaction network further comprises signal transduction pathways.  
   
   
       406 . The computer readable medium or media of  claim 161 , wherein said data structure further comprises at least one equation selected from the group of linear algebraic equation, differential equation and stochastic equation.  
   
   
       407 . The computer readable medium or media of  claim 161 , wherein a first substrate or product in said plurality of reactions is assigned to a first compartment and a second substrate or product in said plurality of reactions is assigned to a second compartment.  
   
   
       408 . The computer readable medium or media of  claim 162 , wherein the regulatory data structure represents a time dependent event.  
   
   
       409 . The computer readable medium or media of  claim 162 , wherein the regulatory data structure represents an event dependent on one or more of said reactions.  
   
   
       410 . The computer readable medium or media of  claim 162 , wherein the regulatory data structure represents an event dependent on the state of one or more of said reactants.  
   
   
       411 . The method of  claim 221  wherein the components of said network are dynamically retrieved from a database of components.  
   
   
       412 . The method of  claim 259 , wherein said at least one network further comprises two or more networks.  
   
   
       413 . The method of  claim 259 , wherein said correlative changes in at least two value sets in said second data integration map further comprise correlative changes in three or more value sets.  
   
   
       414 . The method of  claim 259 , wherein said correlative changes in at least two value sets within said second data integration map further comprise value sets selected from the group consisting of protein expression, polypeptide-polypeptide interaction, nucleic acid-polypeptide interaction, metabolite abundance, and growth rate.  
   
   
       415 . The system of  claim 270  wherein said output network model comprises at least one network component and network process/reaction component representing a component and a process or reaction not intrinsic to said biological system or subsystem.  
   
   
       416 . The system of  claim 270  wherein said data structure comprises reactants and products.  
   
   
       417 . The system of  claim 270  further comprising executable instructions mathematically describing from said data set said network model of connectivity and flow, wherein said mathematical description defines a scalable output network model of the biological system or subsystem.  
   
   
       418 . The system of  claim 417  wherein said data structure comprises reactants, products and stoichiometric coefficients.  
   
   
       419 . The system of  claim 417  wherein said mathematical description comprises stochastic or differential equations, or a combination thereof.