DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Remarks
In response to communications sent October 22, 2025, claim(s) 1-3, 8-10, 15, 21-29, and 31 is/are pending in this application; of these claim(s) 1, 8, and 15 is/are in independent form. Claim(s) 4-7, 11-14, 16-20, and 30 is/are cancelled.
Reopening Prosecution
In view of the appeal brief filed on March 19, 2026, PROSECUTION IS HEREBY REOPENED. A new ground of rejection is set forth below.
To avoid abandonment of the application, appellant must exercise one of the following two options:
(1) file a reply under 37 CFR 1.111 (if this Office action is non-final) or a reply under 37 CFR 1.113 (if this Office action is final); or,
(2) initiate a new appeal by filing a notice of appeal under 37 CFR 41.31 followed by an appeal brief under 37 CFR 41.37. The previously paid notice of appeal fee and appeal brief fee can be applied to the new appeal. If, however, the appeal fees set forth in 37 CFR 41.20 have been increased since they were previously paid, then appellant must pay the difference between the increased fees and the amount previously paid.
A Supervisory Patent Examiner (SPE) has approved of reopening prosecution by signing below:
/OLIVIA M. WISE/ Supervisory Patent Examiner, Art Unit 1685
Information Disclosure Statement
The Information Disclosure Statement(s) is/are acknowledged and the references contained therein have been considered by the Examiner. This includes the Information Disclosure Statements(s) filed on: March 27, 2026.
Response to Arguments
Regarding 35 U.S.C. § 103: Applicant’s arguments, see page 12 line 5 to page 15 line 19, filed March 19, 2026, with respect to the rejection(s) of claim(s) 1-3, 8-10, 15, 21-29, and 31 under 35 U.S.C. § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Klipp as modified by US-20170147722-A1 (“Greenwood”). The Klipp references is Klipp, Edda, et al. Systems biology in practice: concepts, implementation and application. John Wiley & Sons, 2005.
Regarding 35 U.S.C. § 101:
Applicant's arguments filed March 19, 2026 have been fully considered but they are not persuasive.
The claimed invention is a mathematical solution to a mathematical problem, not a technical solution to a technical problem. Hence it is an improvement to mathematical modeling of a natural law, which is an improvement to the judicial exception itself. Applicant argues further that the claims involve modules and an architecture. However, these are mathematical elements. Applicant argues that the iterative nature of the optimization algorithm "improves the functioning of the model itself" (page 10 lines 8-12). However, improvements to a mathematical model are an improvement to the judicial exception itself, and not to an additional element beyond the judicial exception. The "model" is a mathematical model of a law of nature, and is squarely outside of the scope of patent protection.
The claims are not limited to a method that solves the problem of parameter dimensionality. In the event that the “plurality of kinetic parameters” is only two kinetic parameters or each of two reactions, the remainder of the parameters in the model as a whole would still face to the parameter dimensionality problem. The broadest reasonable interpretation of the claims encompasses models where there are a small number of kinetic parameters that are optimized using target values and a large number other kinetic parameters that are not directly compared to the target values and face computational complexity. Hence, the element of linear stability is not reflected in the claim.
Instead, Applicant's claims do not preclude the growth of the number of parameters for interactions between chemicals as the model gets more complex. There are no limitations on the complexity of the model, nor any limitation that would reduce the number of parameters for interactions that would cause the "parameter dimensionality" problem to be solved.
The claims do not recite the improvement involving the vmax/km optimization procedure. Without that element, some of Applicant's arguments are moot, because the claims encompass textbook optimization procedures that do not have linear solutions and continue to have difficult with high dimensionality/complexity.
Applicant argues that there is a particular improvement by using kinetic parameters Vmax and Km and simulating until the simulated Vmax and Km are close to the experimentally measured Vmax and Km parameters. However, this element is not in the current set of claims sent October 22, 2025. That is, the element was expressly deleted in the filing sent October 22, 2025, leading to a Final rejection. That is, the improvement recited in the specification is not recited in the claims filed October 22, 2025, which make no mention of optimization using measured parameters from a Michaelis-Menten experiment. Therefore, technical advantages from the deleted claim limitations are moot. See the interview summary sent April 21, 2025 for a discussion of the technical advantage involve Vmax and Km parameters.
The use of standardized building blocks components, binding-dissociation and transition steps, generating a computational model, and deconstructing the reactions are well-understood, routine, and conventional according to the citations to the Klipp textbook explaining to do this to newcomers to the field of systems biology.
The Apply-It rationale: Applicant argues that the apply-it rationale is not reasonable because of the improvement to the functioning of the computer itself. The Examiner, on the other hand, is arguing that the claimed improvement is an improvement to the mathematics and the computer is merely the platform on which the mathematics is carried out. The Examiner has not "equated technical modeling process with unpatentable algorithms and generic computer components". The examiner has equated a mathematical modeling process with unpatentable mathematical algorithms and generic computer components.
The Desjardins case is not analogous: The precedent of the Desjardins case was about the improvement to the functioning of the computer itself, among other considerations. Whereas in the instant application, the improvement to the mathematics does not improve the computer’s capabilities. It merely, at best, simplifies the mathematical calculations that are applied on the general-purpose computer. That is assuming the claims reflect the mathematical improvement of reducing the dimensionality problem. Notable, unlike Desjardins, there is no improvement to how a neural network operates. And Desjardins does not categorically make all optimization claims patent eligible, and does not categorically make all mathematical optimization claims eligible either.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 8-10, 15, 21-29, and 31 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a law of nature without significantly more. The claim(s) recite(s) systems biology model such that the models can be updated using mathematics and mental processes. For evidence that the claimed invention is an updatable law of nature, the Examiner mapped claim elements of the law of nature to chapter 5 of Klipp, Edda, et al. Systems biology in practice: concepts, implementation and application. John Wiley & Sons, 2005. See below. The Examiner argues that the claim encompasses an improvement to a subfield of “Systems Biology,” which is a law of nature in abstract mathematical detail.
Regarding the independent claims, the judicial exception is not integrated into a practical application because the only element for transmission to a physical system for synthetic construction (i.e. “apply it” and “make it”).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the only element beyond the law of nature are instructions to apply the law of nature on a general-purpose computer The idea of physical implementation of a mathematical model, when recited at a high-level of generality, amounts to no more than extra-solution activity to use the scientific law of nature to engineer a product. The iterative nature of modeling and engineering is also well-understood routine, and conventional. For evidence, see Klipp page 391 Section 12.7. Iterative mathematical modeling is a fundamental tool of science that is not patent eligible.
The improvement noted in the Specification involves using a fitness function where the cost involves a Michaelis-Menten parameter. However, the improvement is not recited nor reflected in the current set of claims. Furthermore, the improvement isn’t the computerization of a technique that has never been computerized before was the case in McRO, Inc. v. Bandai Namco Games America, Inc. 837 F.3d 1299, 1314, 120 USPQ2d 1091, 1102 (Fed. Cir. 2016). Instead, the computerization of systems biological model is well-understood, routine and conventional. See the mapping of the computerized abstract idea to Klipp’s “Systems Biology” textbook (see Klipp Section 12.3: Systems Biology Workbench).
See also the rejection of dependent claims 23, which lack particularly when claiming a transformation. See MPEP § 2106.05(c) regarding the standard of patentability for the recited transformations.
As to claim 1, Klipp teaches a system comprising:
one or more data processors (this is the application of a judicial exception on a general purpose computer, i.e. “apply it” as in “apply the judicial exception” on a general purpose computer); and
a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform operations (this is the application of a judicial exception on a general purpose computer, i.e. “apply it” as in “apply the judicial exception” on a general purpose computer) including:
obtaining a respective target value of each of a plurality of kinetic parameters of a biochemical system that includes a plurality of reactions (part of a mathematical algorithm that does not recite or reflect the technical improvement);
generating a computational model of the biochemical pathway in the biochemical system (judicial exception of a mathematical law of nature; see Klipp section 5.2.1 “Systems Equations”: reactions as part of a metabolic process to be modeled using systems equations, such as Example 5-5 for the upper glycolysis model) comprising:
for each reaction of the plurality of reactions (judicial exception of a mathematical law of nature; Klipp section 5.1.3.1 “How to Derive a Rate Equation”: for each reaction step in a chemical reaction):
deconstructing the reaction into a plurality of component steps for the reaction (judicial exception of a mathematical law of nature; Klipp section 5.1.3.1 “How to Derive a Rate Equation”: Step 1 for identification of all steps in a chemical reaction, such as the chemical reaction illustrated in equation 5-21 in section 5.1.3); and
translating each component step of the plurality of component steps for the reaction into a set of rate equations for the component step to obtain a standard mathematical construct representing each component step for the reaction (judicial exception of a mathematical law of nature; Klipp section 5.1.3.1 “How to Derive a Rate Equation”: Step 2: translating each of the rates of each step into a sum of rates for all steps contributing to a set of ordinary differential equations), wherein the set of rate equations for the component step comprise a forward rate equation and a reverse rate equation (judicial exception of a mathematical law of nature; Klipp section 5.1.3.1: “How to Derive a Rate Equation” Step 2: including forward rates leading to a certain substance and reverse rates leading away from a certain substance), and wherein each forward rate equation and each reverse rate equation is a first-order rate equation or a second-order rate equation parameterized by a respective set of one or more rate equation parameters (judicial exception of a mathematical law of nature; Klipp section 5.1.1 “The Law of Mass Action”: each of the equations for the ordinary differential equations are derived from the Law of Mass Action, which typically involves first-order rate equations having parameters for reaction constants) comprising at least a forward and reverse rate constant (judicial exception of a mathematical law of nature; Klipp Example 5-5 on page 159: inputting a vector of rate constants including forward and reverse reaction rates defined by the corresponding stoichiometry matrix); and
iteratively updating current values of the rate equation parameters associated with the forward and reverse rate equations of each of the component steps of the plurality of reactions, comprising, at each update iteration of a sequence of update iterations (judicial exception of a mathematical law of nature; Klipp page 10 using the page numbers printed on the document, in chapter 1.2.1.9 “Model Development” step 8: “Iterative refinement of model: The initial model will rarely explain all features of the studied object and usually leads to more open questions than answers. After comparing the model outcome with the experimental results, model structure and parameters may be adapted.”):
generating a plurality of simulations of the biochemical pathway, over a range of substrate concentrations, in accordance with the current values of the rate equation parameters (mathematical algorithm that does not recite or reflect the technical improvement);
determining, based on the plurality of simulations of the biochemical pathway, respective predicted values of each of the plurality of kinetic parameters of the biochemical pathway (mathematical algorithm that does not recite or reflect the technical improvement); and
updating the current values of the rate equation parameters for each of the plurality of reactions of the biochemical pathway to reduce an error between: (i) the predicted values of the plurality of kinetic parameters of the biochemical pathway and (ii) the target values of the plurality of kinetic parameters biochemical pathway (mathematical algorithm that does not recite or reflect the technical improvement; the “experimental measurements” are extra-solution activity that do not limit the application of the judicial exception).
As to claim 2, Klipp teaches the system of claim 1, wherein, for each reaction of the plurality of reactions, the plurality of component steps for the reaction comprise at least two binding component steps (judicial exception of a mathematical law of nature; Klipp Section 5.1.3.3 “The Michaelis-Menten Equation for Reversible Reactions”: steps for association with the enzyme and dissociation from the enzyme) and at least one transition component step (judicial exception of a mathematical law of nature; Klipp Section 5.2.7: transition steps may occur during the chemical conversions in the presence of the enzyme), wherein each binding component step of the at least two binding component steps comprises: (i) binding between an enzyme and a substrate (judicial exception of a mathematical law of nature; Klipp Section 5.1.3.3 “The Michaelis-Menten Equation for Reversible Reactions”: binding between and enzyme and a substrate to form an enzyme-substrate in equation 5-35), or (ii) dissociation of an enzyme from a product (judicial exception of a mathematical law of nature; Klipp Section 5.1.3.3 “The Michaelis-Menten Equation for Reversible Reactions”: dissociation of the enzyme from a product in equation 5-35), and wherein each transition component step of the at least one transition component step comprises a chemical conversion of an enzyme:substrate complex to a product:enzyme complex (judicial exception of a mathematical law of nature; Klipp Section 5.2.7: transition steps may occur during the chemical conversions in the presence of the enzyme).
As to claim 3, Klipp teaches the system of claim 1, wherein, for each component step of the plurality of reactions, each forward rate equation for the component step has a form of a forward rate constant kon or kfwd multiplied by a single concentration or two concentrations, and wherein each reverse rate equation for the component step has a form of a reverse rate constant koff or krev multiplied by a single concentration or two concentrations (judicial exception of a mathematical law of nature; Klipp section 5.1.3.3 “The Michaelis-Menten Equation for Reversible Reactions: the forward and reverse equations have a form for the forward reaction corresponding to k1 for the forward rate or k-1 for the reverse rate, the corresponding reactions are multiplied by a single concentration described by respective kinetic laws).
As to claim 8, Klipp teaches a computer-implemented method (the is the application of a judicial exception on a general purpose computer, i.e. “apply it” as in “apply the judicial exception” on a general purpose computer) comprising:
obtaining a respective target value of each of a plurality of kinetic parameters of a biochemical system that includes a plurality of reactions (part of a mathematical algorithm that does not recite or reflect the technical improvement);
generating a computational model of the biochemical a pathway in the biochemical system (judicial exception of a mathematical law of nature; Klipp section 5.2.1 “Systems Equations”: reactions as part of a metabolic process to be modeled using systems equations, such as Example 5-5 for the upper glycolysis model) comprising:
for each reaction of the plurality of reactions (judicial exception of a mathematical law of nature; Klipp section 5.1.3.1 “How to Derive a Rate Equation”: for each reaction step in a chemical reaction):
deconstructing the reaction into a plurality of component steps for the reaction (judicial exception of a mathematical law of nature; Klipp section 5.1.3.1 “How to Derive a Rate Equation”: Step 1 for identification of all steps in a chemical reaction, such as the chemical reaction illustrated in equation 5-21 in section 5.1.3);
translating each component step of the plurality of component steps for the reaction into a set of rate equations for the component step to obtain a standard mathematical construct representing each component step for the reaction (judicial exception of a mathematical law of nature; Klipp section 5.1.3.1 “How to Derive a Rate Equation”: Step 2: translating each of the rates of each step into a sum of rates for all steps contributing to a set of ordinary differential equations), wherein the set of rate equations for the component step comprise a forward rate equation and a reverse rate equation (judicial exception of a mathematical law of nature; Klipp section 5.1.3.1: “How to Derive a Rate Equation” Step 2: including forward rates leading to a certain substance and reverse rates leading away from a certain substance), and wherein each forward rate equation and each reverse rate equation is a first-order rate equation or a second-order rate equation parameterized by a respective set of one or more rate equation parameters (Klipp section 5.1.1 “The Law of Mass Action”: each of the equations for the ordinary differential equations are derived from the Law of Mass Action, which typically involves first-order rate equations having parameters for reaction constants) comprising at least a forward rate constant and a reverse rate constant (Klipp Example 5-5 on page 159: inputting a vector of rate constants including forward and reverse reaction rates defined by the corresponding stoichiometry matrix);
iteratively updating current values of the rate equation parameters associated with the forward and reverse rate equations of each of the component steps of the plurality of reactions, comprising, at each update iteration of a sequence of update iterations (judicial exception of a mathematical law of nature; Klipp page 10 using the page numbers printed on the document, in chapter 1.2.1.9 “Model Development” step 8: “Iterative refinement of model: The initial model will rarely explain all features of the studied object and usually leads to more open questions than answers. After comparing the model outcome with the experimental results, model structure and parameters may be adapted.”):
generating a plurality of simulations of the biochemical pathway, over a range of substrate concentrations, in accordance with the current values of the rate equation parameters (these elements are part of an improvement; however, the improvement is to the judicial exception itself);
determining, based on the plurality of simulations of the biochemical pathway, respective predicted values of each of the plurality of kinetic parameters of the biochemical pathway (mathematical algorithm that does not recite or reflect the technical improvement);
updating the current values of the rate equation parameters for each of the plurality of reactions of the biochemical pathway to reduce an error between (i) the predicted values of the plurality of kinetic parameters of the biochemical pathway and (ii) the target values of the plurality of kinetic parameters of the biochemical pathway (mathematical algorithm that does not recite or reflect the technical improvement; the “experimental measurements” are extra-solution activity that do not limit the application of the judicial exception).
As to claim 9, Klipp teaches the computer-implemented method of claim 8, wherein, for each reaction of the plurality of reactions, the plurality of component steps for the reaction comprise at least two binding component steps (judicial exception of a mathematical law of nature; Klipp Section 5.1.3.3 “The Michaelis-Menten Equation for Reversible Reactions”: steps for association with the enzyme and dissociation from the enzyme) and at least one transition component step (judicial exception of a mathematical law of nature; Klipp Section 5.2.7: transition steps may occur during the chemical conversions in the presence of the enzyme), wherein each binding component step of the at least two binding component steps comprises: (i) binding between an enzyme and a substrate (judicial exception of a mathematical law of nature; Klipp Section 5.1.3.3 “The Michaelis-Menten Equation for Reversible Reactions”: binding between and enzyme and a substrate to form an enzyme-substrate in equation 5-35), or (ii) dissociation of an enzyme from a product (judicial exception of a mathematical law of nature; Klipp Section 5.1.3.3 “The Michaelis-Menten Equation for Reversible Reactions”: dissociation of the enzyme from a product in equation 5-35), and wherein each transition component step of the at least one transition component step comprises a chemical conversion of an enzyme:substrate complex to a product:enzyme complex (judicial exception of a mathematical law of nature; Klipp Section 5.2.7: transition steps may occur during the chemical conversions in the presence of the enzyme).
As to claim 10, Klipp teaches the computer-implemented method of claim 8, wherein for each component step of the plurality of reactions each forward rate equation for the component step has a form of a forward rate constant ko or kfwd multiplied by a single concentration or two concentrations, and wherein each reverse rate equation for the component step has a form of a reverse rate constant koff or krev multiplied by a single concentration or two concentrations (judicial exception of a mathematical law of nature; Klipp section 5.1.3.3 “The Michaelis-Menten Equation for Reversible Reactions: the forward and reverse equations have a form for the forward reaction corresponding to k1 for the forward rate or k-1 for the reverse rate, the corresponding reactions are multiplied by a single concentration described by respective kinetic laws).
As to claim 15, Klipp teaches a computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform actions (the is the application of a judicial exception on a general purpose computer, i.e. “apply it” as in “apply the judicial exception” on a general purpose computer) including:
obtaining a respective target value of each of a plurality of kinetic parameters of a biochemical system that includes a plurality of reactions (part of a mathematical algorithm that does not recite or reflect the technical improvement) ;
generating a computational model the biochemical pathway in the biochemical system (judicial exception of a mathematical law of nature; Klipp section 5.2.1 “Systems Equations”: reactions as part of a metabolic process to be modeled using systems equations, such as Example 5-5 for the upper glycolysis model) comprising:
for each reaction of the plurality of reactions (judicial exception of a mathematical law of nature; Klipp section 5.1.3.1 “How to Derive a Rate Equation”: for each reaction step in a chemical reaction):
deconstructing the reaction into a plurality of component steps for the reaction (judicial exception of a mathematical law of nature; Klipp section 5.1.3.1 “How to Derive a Rate Equation”: Step 1 for identification of all steps in a chemical reaction, such as the chemical reaction illustrated in equation 5-21 in section 5.1.3); and
translating each component step of the plurality of component steps for the reaction into a set of rate equations for the component step to obtain a standard mathematical construct representing each component step for the reaction (Klipp section 5.1.3.1 “How to Derive a Rate Equation”: Step 2: translating each of the rates of each step into a sum of rates for all steps contributing to a set of ordinary differential equations), wherein the set of rate equations for the component step comprise a forward rate equation and a reverse rate equation (Klipp section 5.1.3.1: “How to Derive a Rate Equation” Step 2: including forward rates leading to a certain substance and reverse rates leading away from a certain substance), and wherein each forward rate equation and each reverse rate equation is a first-order rate equation or a second-order rate equation parameterized by a respective set of one or more rate equation parameters (Klipp section 5.1.1 “The Law of Mass Action”: each of the equations for the ordinary differential equations are derived from the Law of Mass Action, which typically involves first-order rate equations) comprising at least a forward rate constant and a reverse rate constant (Klipp Example 5-5 on page 159: inputting a vector of rate constants including forward and reverse reaction rates defined by the corresponding stoichiometry matrix); and
iteratively updating current values of the rate equation parameters associated with the forward and reverse rate equations of each of the component steps of the plurality of reactions, comprising, at each update iteration of a sequence of update iterations (judicial exception of a mathematical law of nature; Klipp page 10 using the page numbers printed on the document, in chapter 1.2.1.9 “Model Development” step 8: “Iterative refinement of model: The initial model will rarely explain all features of the studied object and usually leads to more open questions than answers. After comparing the model outcome with the experimental results, model structure and parameters may be adapted.”):
generating a plurality of simulations of the biochemical pathway, over a range of substrate concentrations, in accordance with the current values of the rate equation parameters (these elements are part of an improvement; however, the improvement is to the judicial exception itself);
determining, based on the plurality of simulations of the biochemical pathway, respective predicted values of each of the plurality of kinetic parameters of the biochemical pathway (mathematical algorithm that does not recite or reflect the technical improvement); and
updating the current values of the rate equation parameters for each of the plurality of reaction of the biochemical pathway to reduce an error between (i) the predicted values of the plurality of kinetic parameters of the biochemical pathway and (ii) the target values of the plurality of kinetic parameters of the biochemical pathway (mathematical algorithm that does not recite or reflect the technical improvement; the “experimental measurements” are extra-solution activity that do not limit the application of the judicial exception).
As to claim 21, Klipp teaches the system of claim 1, the operations further comprising: controlling a physical experimental system based on the computational model of the biochemical pathway (this element an additional element beyond the abstract idea, but the element is a transformation that lacks particularity; see MPEP § 2106.05(c) regarding “The particularity or generality of the transformation”).
As to claim 22, Klipp teaches the system of claim 21, wherein controlling the physical experimental system based on the computational model of the biochemical pathway comprises:
transmitting instructions to the physical experimental system that cause the physical experimental system to perform physical experiments specified using the computational model of the biochemical pathway (see MPEP § 2106.05(d) II, which instructs that transmission of data is considered by the courts to be well-understood, routine, and conventional; “Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362”)
As to claim 23, Klipp teaches the system of claim 22, wherein transmitting instructions to the physical experimental system that cause the physical experimental system to perform physical experiments specified using the computational model of the biochemical pathway comprises:
transmitting, to the physical experimental system (see MPEP § 2106.05(d) II, which instructs that transmission of data is considered by the courts to be well-understood, routine, and conventional; “Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362”), one or more of: initial-state values, parameters, or genetic mutations (judicial exception of a mathematical law of nature; Klipp page 391 Section 12.7 first and second paragraphs: optimization of physical system, which is modeled using the parameters of the in silico model).
As to claim 24, Klipp teaches the system of claim 1, the operations further comprising:
determining, using the computational model of the biochemical pathway, a set of environmental conditions that increases a rate of production of a substance of interest by the pathway (judicial exception of a mathematical law of nature; Klipp page 391 Section 12.7 first and second paragraphs: optimizing the production of energy sources from renewable sources such as plants and microorganisms using the quantitative systems biological approach to reduce the number of physical experiments to manageable number despite the complexity of the biological system).
As to claim 25, Klipp teaches the system of claim 1, the operations further comprising:
determining, using the computational model of the biochemical pathway, a change in a genome of an organism that increases a rate of production of a substance of interest by the biochemical pathway (judicial exception of a mathematical law of nature; note that this step is a “determination” in which no actual substances are affected; Klipp page 391 Section 12.7 first and second paragraphs: optimizing the production of energy sources from renewable sources such as plants and microorganisms using the quantitative systems biological approach to reduce the number of physical experiments to manageable number despite the complexity of the biological system).
26. The system of claim 25, wherein the substance of interest is an antibody (a limitation to a judicial exception of a mathematical law of nature; note that this part of a step that is a “determination” in which no actual substances are affected).
27. The system of claim 25, wherein the substance of interest is a hormone (a limitation to a judicial exception of a mathematical law of nature; note that this part of a step that is a “determination” in which no actual substances are affected).
28. The system of claim 25, wherein the substance of interest is a protein (a limitation to a judicial exception of a mathematical law of nature; note that this part of a step that is a “determination” in which no actual substances are affected).
29. The system of claim 25, wherein the system of interest is an enzyme (a limitation to a judicial exception of a mathematical law of nature; note that this part of a step that is a “determination” in which no actual substances are affected).
31. The method of claim 8, further comprising controlling a physical experimental system based on the computational model of the biochemical pathway (this machine or transformation is lacks particularity; MPEP § 2106.05(b) and MPEP § 2106.05(c)).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 8-10, 15, 21-29, and 31 is/are rejected under 35 U.S.C. 103 as being unpatentable over Klipp in view of US-20170147722-A1 (“Greenwood”).
Klipp refers to Klipp, Edda, et al. Systems biology in practice: concepts, implementation and application. John Wiley & Sons, 2005.
As to claim 1, Klipp teaches a system comprising:
one or more data processors (Klipp Section 12.3: Systems Biology Workbench); and
a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform operations (Klipp Section 12.3: Systems Biology Workbench) including:
obtaining a respective target value [[ (Klipp Section 10.3 on page 356 on lines 10-15: “In order to take into account biological constraints, the concept of a cost function has been introduced (Reich 1983). The following have been suggested as cost functions: the total amount of enzyme in a cell or the pathway under consideration(Reich 1983), the total energy utilization (Stucki 1980), or the evolutionary effort(Heinrich and Holzhutter 1985) counting the number of mutations or events necessary to attain a certain state.”);
generating a computational model of the biochemical pathway in the biochemical system (Klipp section 5.2.1 “Systems Equations”: reactions as part of a metabolic process to be modeled using systems equations, such as Example 5-5 for the upper glycolysis model) comprising:
for each reaction of the plurality of reactions (Klipp section 5.1.3.1 “How to Derive a Rate Equation”: for each reaction step in a chemical reaction):
deconstructing the reaction into a plurality of component steps for the reaction (Klipp section 5.1.3.1 “How to Derive a Rate Equation”: Step 1 for identification of all steps in a chemical reaction, such as the chemical reaction illustrated in equation 5-21 in section 5.1.3); and
translating each component step of the plurality of component steps for the reaction into a set of rate equations for the component step to obtain a standard mathematical construct representing each component step for the reaction (Klipp section 5.1.3.1 “How to Derive a Rate Equation”: Step 2: translating each of the rates of each step into a sum of rates for all steps contributing to a set of ordinary differential equations), wherein the set of rate equations for the component step comprise a forward rate equation and a reverse rate equation (Klipp section 5.1.3.1: “How to Derive a Rate Equation” Step 2: including forward rates leading to a certain substance and reverse rates leading away from a certain substance), and wherein each forward rate equation and each reverse rate equation is a first-order rate equation or a second-order rate equation parameterized by a respective set of one or more rate equation parameters (Klipp section 5.1.1 “The Law of Mass Action”: each of the equations for the ordinary differential equations are derived from the Law of Mass Action, which typically involves first-order rate equations having parameters for reaction constants) comprising at least a forward and reverse rate constant (Klipp Example 5-5 on page 159: inputting a vector of rate constants including forward and reverse reaction rates defined by the corresponding stoichiometry matrix); and
iteratively updating current values of the rate equation parameters associated with the forward and reverse rate equations of each of the component steps of the plurality of reactions, comprising, at each update iteration of a sequence of update iterations (Klipp page 10 using the page numbers printed on the document, in chapter 1.2.1.9 “Model Development” step 8: “Iterative refinement of model: The initial model will rarely explain all features of the studied object and usually leads to more open questions than answers. After comparing the model outcome with the experimental results, model structure and parameters may be adapted.”):
generating a plurality of simulations of the biochemical pathway, over a range of substrate concentrations, in accordance with the current values of the rate equation parameters (Klipp section 5.1.1: “The Law of Mass Action”: the time course of the individual chemicals are obtained by integrating the ordinary differential equations, which by definition, determines incremental computations of the individual steps of the ordinary differential equations; in the system of equations in Klipp Chapter 5, the steps determine how the steps of the reactions contribute to a rate of change of molecules within the pathway);
determining, based on the plurality of simulations of the biochemical pathway, respective predicted values [[ (Klipp Section 5.2.8 “Approximations Based on Timescale Separation”: deriving quasi steady-state behavior based on the simulation of the ordinary differential equations, the simulation comprising component steps of the rate of the molecules as noted above in the mapping to section 5.1.3.1 “How to Derive a Rate Equation”); and
updating the current values of the rate equation parameters for each of the plurality of reactions of the biochemical pathway to reduce an error between: (i) the predicted values [[ (Klipp Section 10.3 on page 356 on lines 10-15: “In order to take into account biological constraints, the concept of a cost function has been introduced (Reich 1983). The following have been suggested as cost functions: the total amount of enzyme in a cell or the pathway under consideration(Reich 1983), the total energy utilization (Stucki 1980), or the evolutionary effort(Heinrich and Holzhutter 1985) counting the number of mutations or events necessary to attain a certain state.”).
However, Klipp does not teach that the quantities for which error amount is minimized are based on the kinetic parameters of the simulations.
Nevertheless, Greenwood teaches a simulation in systems biology using kinetic parameters to compute the error amount during optimization (Greenwood Para [0591]-[0592]: “writing the notional error between the system's “true” parameter vector and the model's parameter vector … such that the observed system dynamics and the model dynamics converge”).
Klipp and Greenwood are in the same field of systems biology. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Klipp to include the teachings of Greenwood for more accurate systems behavior (See Greenwood, Abstract). There would be an expectation of success because each reference uses error minimization to find optimal parameter set.
As to claim 2, Klipp in view of Greenwood teaches the system of claim 1, wherein, for each reaction of the plurality of reactions, the plurality of component steps for the reaction comprise at least two binding component steps (Klipp Section 5.1.3.3 “The Michaelis-Menten Equation for Reversible Reactions”: steps for association with the enzyme and dissociation from the enzyme) and at least one transition component step (Klipp Section 5.2.7: transition steps may occur during the chemical conversions in the presence of the enzyme), wherein each binding component step of the at least two binding component steps comprises: (i) binding between an enzyme and a substrate (Klipp Section 5.1.3.3 “The Michaelis-Menten Equation for Reversible Reactions”: binding between and enzyme and a substrate to form an enzyme-substrate in equation 5-35), or (ii) dissociation of an enzyme from a product (Klipp Section 5.1.3.3 “The Michaelis-Menten Equation for Reversible Reactions”: dissociation of the enzyme from a product in equation 5-35), and wherein each transition component step of the at least one transition component step comprises a chemical conversion of an enzyme:substrate complex to a product:enzyme complex (Klipp Section 5.2.7: transition steps may occur during the chemical conversions in the presence of the enzyme).
As to claim 3, Klipp in view of Greenwood teaches the system of claim 1, wherein, for each component step of the plurality of reactions, each forward rate equation for the component step has a form of a forward rate constant kon or kfwd multiplied by a single concentration or two concentrations, and wherein each reverse rate equation for the component step has a form of a reverse rate constant koff or krev multiplied by a single concentration or two concentrations (Klipp section 5.1.3.3 “The Michaelis-Menten Equation for Reversible Reactions: the forward and reverse equations have a form for the forward reaction corresponding to k1 for the forward rate or k-1 for the reverse rate, the corresponding reactions are multiplied by a single concentration described by respective kinetic laws).
As to claim 8, Klipp teaches a computer-implemented method (Klipp Section 12.3: Systems Biology Workbench) comprising:
obtaining a respective target value [[ (Klipp Section 10.3 on page 356 on lines 10-15: “In order to take into account biological constraints, the concept of a cost function has been introduced (Reich 1983). The following have been suggested as cost functions: the total amount of enzyme in a cell or the pathway under consideration(Reich 1983), the total energy utilization (Stucki 1980), or the evolutionary effort(Heinrich and Holzhutter 1985) counting the number of mutations or events necessary to attain a certain state.”);
generating a computational model of the biochemical a pathway in the biochemical system (Klipp section 5.2.1 “Systems Equations”: reactions as part of a metabolic process to be modeled using systems equations, such as Example 5-5 for the upper glycolysis model) comprising:
for each reaction of the plurality of reactions (Klipp section 5.1.3.1 “How to Derive a Rate Equation”: for each reaction step in a chemical reaction):
deconstructing the reaction into a plurality of component steps for the reaction (Klipp section 5.1.3.1 “How to Derive a Rate Equation”: Step 1 for identification of all steps in a chemical reaction, such as the chemical reaction illustrated in equation 5-21 in section 5.1.3);
translating each component step of the plurality of component steps for the reaction into a set of rate equations for the component step to obtain a standard mathematical construct representing each component step for the reaction (Klipp section 5.1.3.1 “How to Derive a Rate Equation”: Step 2: translating each of the rates of each step into a sum of rates for all steps contributing to a set of ordinary differential equations), wherein the set of rate equations for the component step comprise a forward rate equation and a reverse rate equation (Klipp section 5.1.3.1: “How to Derive a Rate Equation” Step 2: including forward rates leading to a certain substance and reverse rates leading away from a certain substance), and wherein each forward rate equation and each reverse rate equation is a first-order rate equation or a second-order rate equation parameterized by a respective set of one or more rate equation parameters (Klipp section 5.1.1 “The Law of Mass Action”: each of the equations for the ordinary differential equations are derived from the Law of Mass Action, which typically involves first-order rate equations having parameters for reaction constants) comprising at least a forward rate constant and a reverse rate constant (Klipp Example 5-5 on page 159: inputting a vector of rate constants including forward and reverse reaction rates defined by the corresponding stoichiometry matrix);
iteratively updating current values of the rate equation parameters associated with the forward and reverse rate equations of each of the component steps of the plurality of reactions, comprising, at each update iteration of a sequence of update iterations (Klipp page 10 using the page numbers printed on the document, in chapter 1.2.1.9 “Model Development” step 8: “Iterative refinement of model: The initial model will rarely explain all features of the studied object and usually leads to more open questions than answers. After comparing the model outcome with the experimental results, model structure and parameters may be adapted.”):
generating a plurality of simulations of the biochemical pathway, over a range of substrate concentrations, in accordance with the current values of the rate equation parameters (Klipp section 5.1.1: “The Law of Mass Action”: the time course of the individual chemicals are obtained by integrating the ordinary differential equations, which by definition, determines incremental computations of the individual steps of the ordinary differential equations; in the system of equations in Klipp Chapter 5, the steps determine how the steps of the reactions contribute to a rate of change of molecules within the pathway);
determining, based on the plurality of simulations of the biochemical pathway, respective predicted values of each [[ (Klipp Section 5.2.8 “Approximations Based on Timescale Separation”: deriving quasi steady-state behavior based on the simulation of the ordinary differential equations, the simulation comprising component steps of the rate of the molecules as noted above in the mapping to section 5.1.3.1 “How to Derive a Rate Equation”);
updating the current values of the rate equation parameters for each of the plurality of reactions of the biochemical pathway to reduce an error between (i) the predicted values [[ (Klipp Section 10.3 on page 356 on lines 10-15: “In order to take into account biological constraints, the concept of a cost function has been introduced (Reich 1983). The following have been suggested as cost functions: the total amount of enzyme in a cell or the pathway under consideration(Reich 1983), the total energy utilization (Stucki 1980), or the evolutionary effort(Heinrich and Holzhutter 1985) counting the number of mutations or events necessary to attain a certain state.”).
However, Klipp does not teach that the quantities for which error amount is minimized are based on the kinetic parameters of the simulations.
Nevertheless, Greenwood teaches a simulation in systems biology using kinetic parameters to compute the error amount during optimization (Greenwood Para [0591]-[0592]: “writing the notional error between the system's “true” parameter vector and the model's parameter vector … such that the observed system dynamics and the model dynamics converge”).
Klipp and Greenwood are in the same field of systems biology. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Klipp to include the teachings of Greenwood for more accurate systems behavior (See Greenwood, Abstract). There would be an expectation of success because each reference uses error minimization to find optimal parameter set.
As to claim 9, Klipp in view of Cottier teaches the computer-implemented method of claim 8, wherein, for each reaction of the plurality of reactions, the plurality of component steps for the reaction comprise at least two binding component steps (Klipp Section 5.1.3.3 “The Michaelis-Menten Equation for Reversible Reactions”: steps for association with the enzyme and dissociation from the enzyme) and at least one transition component step (Klipp Section 5.2.7: transition steps may occur during the chemical conversions in the presence of the enzyme), wherein each binding component step of the at least two binding component steps comprises: (i) binding between an enzyme and a substrate (Klipp Section 5.1.3.3 “The Michaelis-Menten Equation for Reversible Reactions”: binding between and enzyme and a substrate to form an enzyme-substrate in equation 5-35), or (ii) dissociation of an enzyme from a product (Klipp Section 5.1.3.3 “The Michaelis-Menten Equation for Reversible Reactions”: dissociation of the enzyme from a product in equation 5-35), and wherein each transition component step of the at least one transition component step comprises a chemical conversion of an enzyme:substrate complex to a product:enzyme complex (Klipp Section 5.2.7: transition steps may occur during the chemical conversions in the presence of the enzyme).
As to claim 10, Klipp in view of Cottier teaches the computer-implemented method of claim 8, wherein for each component step of the plurality of reactions each forward rate equation for the component step has a form of a forward rate constant ko or kfwd multiplied by a single concentration or two concentrations, and wherein each reverse rate equation for the component step has a form of a reverse rate constant koff or krev multiplied by a single concentration or two concentrations (Klipp section 5.1.3.3 “The Michaelis-Menten Equation for Reversible Reactions: the forward and reverse equations have a form for the forward reaction corresponding to k1 for the forward rate or k-1 for the reverse rate, the corresponding reactions are multiplied by a single concentration described by respective kinetic laws).
As to claim 15, Klipp teaches a computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform actions (Klipp Section 12.3: Systems Biology Workbench) including:
obtaining a respective target value [[ (Klipp Section 10.3 on page 356 on lines 10-15: “In order to take into account biological constraints, the concept of a cost function has been introduced (Reich 1983). The following have been suggested as cost functions: the total amount of enzyme in a cell or the pathway under consideration(Reich 1983), the total energy utilization (Stucki 1980), or the evolutionary effort(Heinrich and Holzhutter 1985) counting the number of mutations or events necessary to attain a certain state.”);
generating a computational model the biochemical pathway in the biochemical system (Klipp section 5.2.1 “Systems Equations”: reactions as part of a metabolic process to be modeled using systems equations, such as Example 5-5 for the upper glycolysis model) comprising:
for each reaction of the plurality of reactions (Klipp section 5.1.3.1 “How to Derive a Rate Equation”: for each reaction step in a chemical reaction):
deconstructing the reaction into a plurality of component steps for the reaction (Klipp section 5.1.3.1 “How to Derive a Rate Equation”: Step 1 for identification of all steps in a chemical reaction, such as the chemical reaction illustrated in equation 5-21 in section 5.1.3); and
translating each component step of the plurality of component steps for the reaction into a set of rate equations for the component step to obtain a standard mathematical construct representing each component step for the reaction (Klipp section 5.1.3.1 “How to Derive a Rate Equation”: Step 2: translating each of the rates of each step into a sum of rates for all steps contributing to a set of ordinary differential equations), wherein the set of rate equations for the component step comprise a forward rate equation and a reverse rate equation (Klipp section 5.1.3.1: “How to Derive a Rate Equation” Step 2: including forward rates leading to a certain substance and reverse rates leading away from a certain substance), and wherein each forward rate equation and each reverse rate equation is a first-order rate equation or a second-order rate equation parameterized by a respective set of one or more rate equation parameters (Klipp section 5.1.1 “The Law of Mass Action”: each of the equations for the ordinary differential equations are derived from the Law of Mass Action, which typically involves first-order rate equations) comprising at least a forward rate constant and a reverse rate constant (Klipp Example 5-5 on page 159: inputting a vector of rate constants including forward and reverse reaction rates defined by the corresponding stoichiometry matrix); and
iteratively updating current values of the rate equation parameters associated with the forward and reverse rate equations of each of the component steps of the plurality of reactions, comprising, at each update iteration of a sequence of update iterations (Klipp page 10 using the page numbers printed on the document, in chapter 1.2.1.9 “Model Development” step 8: “Iterative refinement of model: The initial model will rarely explain all features of the studied object and usually leads to more open questions than answers. After comparing the model outcome with the experimental results, model structure and parameters may be adapted.”):
generating a plurality of simulations of the biochemical pathway, over a range of substrate concentrations, in accordance with the current values of the rate equation parameters (Klipp section 5.1.1: “The Law of Mass Action”: the time course of the individual chemicals are obtained by integrating the ordinary differential equations, which by definition, determines incremental computations of the individual steps of the ordinary differential equations; in the system of equations in Klipp Chapter 5, the steps determine how the steps of the reactions contribute to a rate of change of molecules within the pathway);
determining, based on the plurality of simulations of the biochemical pathway, respective predicted values [[ (Klipp Section 5.2.8 “Approximations Based on Timescale Separation”: deriving quasi steady-state behavior based on the simulation of the ordinary differential equations, the simulation comprising component steps of the rate of the molecules as noted above in the mapping to section 5.1.3.1 “How to Derive a Rate Equation”); and
updating the current values of the rate equation parameters for each of the plurality of reaction of the biochemical pathway to reduce an error between (i) the predicted values [[ (Klipp Section 10.3 on page 356 on lines 10-15: “In order to take into account biological constraints, the concept of a cost function has been introduced (Reich 1983). The following have been suggested as cost functions: the total amount of enzyme in a cell or the pathway under consideration(Reich 1983), the total energy utilization (Stucki 1980), or the evolutionary effort(Heinrich and Holzhutter 1985) counting the number of mutations or events necessary to attain a certain state.”).
However, Klipp does not teach that the quantities for which error amount is minimized are based on the kinetic parameters of the simulations.
Nevertheless, Cottier teaches a simulation in systems biology using kinetic parameters to compute the error amount during optimization (See Cottier title, abstract, and Para [0019]).
Klipp and Cottier are in the same field of systems biology. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Klipp to include the teachings of Cottier because of reduced computational intensity (See Cottier Para [0060]-[0061]). There would be an expectation of success because the greedy optimization techniques have general applicability for finding local maxima.
As to claim 21, Klipp in view of Cottier teaches the system of claim 1, the operations further comprising: controlling a physical experimental system based on the computational model of the biochemical pathway (Klipp page 391 Section 12.7 first and second paragraphs: optimizing the production of energy sources from renewable sources such as plants and microorganisms using the quantitative systems biological approach to reduce the number of physical experiments to manageable number despite the complexity of the biological system).
As to claim 22, Klipp in view of Cottier teaches the system of claim 21, wherein controlling the physical experimental system based on the computational model of the biochemical pathway comprises:
transmitting instructions (Klipp Section 12.3: Systems Biology Workbench involves at least one transmission to output from the software) to the physical experimental system that cause the physical experimental system to perform physical experiments specified using the computational model of the biochemical pathway (Klipp page 391 Section 12.7 first and second paragraphs: optimizing the production of energy sources from renewable sources such as plants and microorganisms using the quantitative systems biological approach to reduce the number of physical experiments to manageable number despite the complexity of the biological system).
As to claim 23, Klipp in view of Cottier teaches the system of claim 22, wherein transmitting instructions (Klipp Section 12.3: Systems Biology Workbench involves at least one transmission to output from the software) to the physical experimental system that cause the physical experimental system to perform physical experiments specified using the computational model of the biochemical pathway (Klipp page 391 Section 12.7 first and second paragraphs: optimizing the production of energy sources from renewable sources such as plants and microorganisms using the quantitative systems biological approach to reduce the number of physical experiments to manageable number despite the complexity of the biological system) comprises:
transmitting, to the physical experimental system (Klipp Section 12.3: Systems Biology Workbench involves at least one transmission to output from the software), one or more of: initial-state values, parameters, or genetic mutations (Klipp page 391 Section 12.7 first and second paragraphs: optimization of physical system, which is modeled using the parameters of the in silico model).
As to claim 24, Klipp in view of Cottier teaches the system of claim 1, the operations further comprising:
determining, using the computational model of the biochemical pathway, a set of environmental conditions that increases a rate of production of a substance of interest by the pathway (Klipp page 391 Section 12.7 first and second paragraphs: optimizing the production of energy sources from renewable sources such as plants and microorganisms using the quantitative systems biological approach to reduce the number of physical experiments to manageable number despite the complexity of the biological system)..
As to claim 25, Klipp in view of Cottier teaches the system of claim 1, the operations further comprising:
determining, using the computational model of the biochemical pathway, a change in a genome of an organism that increases a rate of production of a substance of interest by the biochemical pathway (Klipp page 391 Section 12.7 first and second paragraphs: optimizing the production of energy sources from renewable sources such as plants and microorganisms using the quantitative systems biological approach to reduce the number of physical experiments to manageable number despite the complexity of the biological system).
As to claim 26, Klipp in view of Cottier teaches the system of claim 25, wherein the substance of interest is an antibody (note that this part of a step that is a “determination” in which no actual substances are affected; the Examiner argues that this is an intended result that doesn’t change the computational system’s steps or calculations, it just changes the semantic meanings of the quantities of the variables that are not semantically represented in the model; hence, various common chemicals are at once envisaged; nevertheless, see Klipp page 132 Figure 4.10 for a systems model involving antibodies).
As to claim 27, Klipp in view of Cottier teaches the system of claim 25, wherein the substance of interest is a hormone (Klipp page 206 Section 6.3.1 first paragraph: modeling of systems where a hormone is of interest; note that this part of a step that is a “determination” in which no actual substances are affected; the Examiner argues that this is an intended result that doesn’t change the computational system’s steps or calculations, it just changes the semantic meanings of the quantities of the variables that are not semantically represented in the model; hence, various common chemicals are at once envisaged).
As to claim 28, Klipp in view of Cottier teaches the system of claim 25, wherein the substance of interest is a protein (Klipp page 155 Section 5.1.10 first paragraph: a multi-subunit enzyme, which is typically a protein; note that this part of a step that is a “determination” in which no actual substances are affected; the Examiner argues that this is an intended result that doesn’t change the computational system’s steps or calculations, it just changes the semantic meanings of the quantities of the variables that are not semantically represented in the model; hence, various common chemicals are at once envisaged).
As to claim 29, Klipp in view of Cottier teaches the system of claim 25, wherein the system of interest is an enzyme (Klipp page 155 Section 5.1.10 first paragraph: an enzyme system; note that this part of a step that is a “determination” in which no actual substances are affected; the Examiner argues that this is an intended result that doesn’t change the computational system’s steps or calculations, it just changes the semantic meanings of the quantities of the variables that are not semantically represented in the model; hence, various common chemicals are at once envisaged).
As to claim 31, Klipp in view of Cottier teaches the method of claim 8, further comprising controlling a physical experimental system based on the computational model of the biochemical pathway (Klipp page 391 Section 12.7 first and second paragraphs: optimization of physical system, which is modeled using the parameters of the in silico model).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20190005187 A1 (“Costello”), the reference frequently mentions Michaelis-Menten models as ground truth. However, Para [0120] of Costello gives an example where the time points of proteomic data is used for model fitting, rather than an error minimization involving the Michaelis-Menten parameters themselves. Para Costello [0009] suggests model fitting using timepoints and their derivatives, rather than Michaelis-Menten parameters as part of the cost function. Also pertinent to an unclaimed feature, the use of neural networks to optimize a metabolic model.
CN-108021787-A (with machine translation): involves using model constraints to eliminate model redundancy, rather than fitting models based on an error function of the Michaelis-Menten constants.
Youseph, A. S. K., Chetty, M., & Karmakar, G. (2015, May). Gene regulatory network inference using Michaelis-Menten kinetics. In 2015 IEEE Congress on Evolutionary Computation (CEC) (pp. 2392-2397). IEEE. (Year: 2015)
The Youseph reference is about setting up a model topology involving Michaelis-Menten kinetics.
WO 2005111905 A2: Pertinent because of the Systems Biology Workbench as part of the implementation of a systems-biology modeling system
US 2007/0250299 A1: Pertinent as a MathWorks implementation of systems biologic concepts
US 8,521,438 B1: Pertinent as a MathWorks implementation of systems biologic concepts
US 2017/0147742 A1: Pertinent systems biology application.
US 9,405,863 B1: Pertinent systems biology application.
US 20140188450 A1: Pertinent systems biology application.
US 20060136138 A1: Pertinent systems biology application.
US 20050114398 A1: Pertinent systems biology application. Sauro, Herbert M., et al. "Next generation simulation tools: the Systems Biology Workbench and BioSPICE integration." Omics A Journal of Integrative Biology 7.4 (2003): 355-372.
Francois, Paul, and Vincent Hakim. "Design of genetic networks with specified functions by evolution in silico." Proceedings of the National Academy of Sciences 101.2 (2004): 580-585. (Year: 2004)
Paladugu, S. R., et al. "In silico evolution of functional modules in biochemical networks." IEE Proceedings-Systems Biology 153.4 (2006): 223-235. (Year: 2006)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jesse P Frumkin whose telephone number is (571)270-1849. The examiner can normally be reached Monday - Friday, 10-5 ET.
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/JESSE P FRUMKIN/Primary Examiner, Art Unit 1685 May 11, 2026
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