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. Status of the Claims Claims 1-10, 12-19, 26, and 28 are pending and under consideration in this action. Claims 11, 20-25, 27, and 29-30 were canceled in the amendment filed 12/9/2022. Priority The instant application claims domestic benefit to U.S. Provisional Application No. 63/246,114 , filed 9/20/2021, as reflected in the filing receipt mailed 12/15/2022 . The claim for domestic benefit for claims 1-10, 12-19, 26, and 28 is acknowledged. As such, the effective filing date of claims 1-10, 12-19, 26, and 28 is 9/20/2021 . Information Disclosure Statement The information disclosure statement (IDS) submitted on 8/15/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS has been considered by the examiner. Specification The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. The following title is suggested: Kinetic learning to predict metabolic behavior using machine learning and time-series multiomics data . Claim Objections Claims 17 and 28 are objected to because of the following informalities: Claim 17 recites the phrase “ a method for stimulating a strain of an organism ”, which should be corrected to “ a method for simulating a strain of an organism ” for clarity. Claim 28 recites the phrase “ a method for determining modifications of protein expression an organism ”, which should be corrected to “ a method for determining modifications of protein expression of an organism ” for clarity. Claim 28 also recites the phrase “ comprising time-series proteomics data of comprising a characteristic of each… ”, which should be corrected to “ comprising time-series proteomics data of comprising a characteristic of each… ” for clarity. Appropriate correction is required. 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 1-10, 12-19, 26, and 28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite both (1) mathematical concepts (mathematical relationships, formulas or equations, or mathematical calculations) and (2) mental processes, i.e., concepts performed in the human mind (including observations, evaluations, judgements or opinions) (see MPEP § 2106.04(a)). Step 1: In the instant application, claims 1-10 and 12-16 are directed towards a system, and claims 17-19, 26, and 28 are directed towards a process , which falls into one of the categories of statutory subject matter ( Step 1: YES ). Step 2A , Prong One: In accordance with MPEP § 2106, claims found to recite statutory subject matter ( Step 1: YES ) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon ( Step 2A , Prong One ). The following instant claims recite limitations that equate to one or more categories of judicial exceptions: Claim 1 recites a mental process (i.e., an evaluation of the multiomics data) in “ wherein the times-series multiomics data comprises time-series proteomics data of a plurality of proteins and time-series metabolomics data comprising a c haracteristic of a metabolite ”; a mathematical concept (i.e., training a model; it is noted that the machine learning model can be a linear regression model, a logistic regression model, a decision tree, a random forest model , or a partial least squares model as described in Specification Para. [0008]) in “ training a machine learning model with the time-series proteomics data as input and the time-series metabolomics data of the metabolite as output ”; and a mathematical concept (i.e., using the model) and a mental process (i.e., an evaluation of the model output) in “ simulating a virtual strain of the organism using the machine learning model to determine the characteristic of the metabolite in the virtual strain ”. Claim 2 recites a mental process (i.e., an evaluation of the multiomics data) in “ wherein the time-series multiomics data comprises time-series multiomics data of a plurality of strains of the organism ”. Claim 3 recites a mental process (i.e., an evaluation of the proteomics data) in “ wherein the time-series proteomics data is associated with a metabolic pathway ”. Claim 4 recites a mental process (i.e., an evaluation of the metabolic pathway) in “ wherein the metabolic pathway comprises a heterologous pathway ”. Claim 5 recites a mental process (i.e., an evaluation of the model) in “ wherein the machine learning model represents kinetics of the metabolic pathway ”. Claim 6 recites a mental process (i.e., an evaluation of the characteristic output from the model) in “ wherein the characteristic of the metabolite is a titer, rate, concentration, or yield of the metabolite ” . Claim 7 recites a mental process (i.e., an evaluation of the proteomics data and metabolomics data) in “ wherein the proteomics data comprises a concentration of each of a plurality of proteins at each of a plurality of time points, and wherein the metabolomics data comprises a concentration of the metabolite at each of the plurality of time points ” . Claim 8 recites a mental process (i.e., an evaluation of the multiomics data) in “ wherein the multiomics data comprises triplicates of a concentration of a protein at a time point and triplicates of a concentration of the metabolite at a time point ” . Claim 9 recites a mathematical concept (i.e., using the model) and a mental process (i.e., evaluating the output of the model) in “ wherein simulating the virtual strain of the organism comprises determining a concentration of the metabolite of the virtual strain using the machine learning model ” . Claim 10 recites a mental process (i.e., an evaluation of the model) in “ wherein the machine learning model comprises a supervised machine learning model, a non-classification model, a neural network, a recurrent neural network ( RNN ), a linear regression model, a logistic regression model, a decision tree, a support vector machine, a Naive Bayes network, a k-nearest neighbors (KNN) model, a k-means model, a random forest model, a multilayer perceptron, or a combination thereof ”. Claim 12 recites a mental process (i.e., an evaluation of the model) in “ wherein the machine learning model comprises a deep neural network ( DNN ), deep recurrent neural network ( DRNN ), gated recurrent unit (GRU) DRNN , a partial least square (PLS) model, or a combination thereof ” . Claim 13 recites a mental process (i.e., an evaluation of the model) in “ wherein the machine learning model comprises an ensemble model of a plurality of machine learning models, optionally wherein the plurality of machine learning models comprises a deep neural network ( DNN ), deep recurrent neural network ( DRNN ), and gated recurrent unit (GRU) DRNN ” . Claim 14 recites a mental process (i.e., an evaluation of the virtual strain output) in “ wherein the virtual strain comprises an increased expression of at least one first protein, a knock-out of at least one second protein, a reduced expression of at least one third protein, or a combination thereof, optionally wherein the at least one first protein comprises at least 10 first proteins, optionally wherein the at least one second protein comprises at least 10 second proteins, optionally wherein the at least one third protein comprises at least 10 third proteins ”. Claim 15 recites a mathematical concept (i.e., retraining the model; see note for claim 1 above for model classification) in “ retraining the machine learning model based on the experimental time-series multiomics data of the new strains ”. Claim 16 recites a mathematical concept (i.e., interpolating data) in “ interpolating the timeseries multiomics data from a first number of time points to a second number of time points, optionally wherein the first number of time points comprises 8 time points, optionally wherein the second number of time points comprises 63 time points, optionally wherein the first number of time points are hourly time points, optionally wherein the second number of time points are hourly time points, and optionally wherein interpolating the time-series multiomics data comprises interpolating the time-series multiomics data using a cubic spline method ”. Claim 17 recites a mathematical concept (i.e., training a model; it is noted that the machine learning model can be a linear regression model, a logistic regression model, a decision tree, a random forest model , or a partial least squares model as described in Specification Para. [0008]) in “ training a machine learning model with the time-series proteomics data as input and the time-series metabolomics data of the metabolite as output ”; and a mathematical concept (i.e., using the model) and a mental process (i.e., an evaluation of the model output) in “ simulating a virtual strain of the organism using the machine learning model to determine the characteristic of the metabolite in the virtual strain ”. Claim 18 recites a mental process (i.e., an evaluation of the data for preprocessing) in “ wherein receiving the time-series multiomics data comprises data checking and/or preprocessing of the time-series multiomics data of the plurality of strains of the organism ” . Claim 19 recites a mental process (i.e., an evaluation of the multiomics data) in “ wherein the time-series multiomics data comprises multiomics data of two or more time-series of a strain ” . Claim 28 recites a mathematical concept (i.e., training a model; it is noted that the machine learning model can be a linear regression model, a logistic regression model, a decision tree, a random forest model , or a partial least squares model as described in Specification Para. [0008]) in “ training a machine learning model with the time-series proteomics data as input and the time-series metabolomics data of the metabolite as output ”; and a mathematical concept (i.e., using the model) and a mental process (i.e., an evaluation of the model output) in “ determining modifications of a concentration of each of one or more proteins using the machine learning model ”. These recitations are similar to the concepts of collecting information, and displaying certain results of the collection and analysis is Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)), and organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships. The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification, and are determined to be directed to mental processes that in the simplest embodiments are not too complex to practically perform in the human mind. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind. Specifically, claims 1, 17, and 28 involve nothing more than training a machine learning model and using the machine learning model to determine a characteristic or property. The step reciting “ a machine learning model ” is, under the BRI, performed using mathematical operations. The instant Specification (see Para. [0008]) discloses that the machine learning model can be a linear regression model, a logistic regression model, a decision tree, a random forest model , or a partial least squares model. Additionally, since there are no specifics in the methodology , determining a characteristic or property from the output of the model is something that, under BRI, one can perform mentally. Therefore, the claimed steps are not further defined beyond something that reads on performing calculations using a computer as a tool, and making a determination based on the output. As such, said steps are directed to judicial exceptions. T he instant claims must therefore be examined further to determine whether they integrate the abstract idea into a practical application ( Step 2A , Prong One: YES ). Step 2A , Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP § 2106.04(d)). A claim that integrates a judicial exception into a practical application will appl y , rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP § 2106.04(d)(I)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP § 2106.04(d)(III)). The following independent claims recite limitations that equate to additional elements: Claim 1 recites “ computer-readable memory storing executable instructions and time-series multiomics data of an organism ” and “ one or more hardware processors programmed by the executable instructions to perform steps ”. Claim 17 recites “ receiving time-series multiomics data of a plurality of strains of an organism comprising time-series proteomics data of a plurality of proteins and time-series metabolomics data comprising a characteristic of a metabolite ”. Claim 28 recites “ receiving time-series multiomics data of a plurality of strains of an organism comprising time-series proteomics data of comprising a characteristic of each of a plurality of proteins and time-series metabolomics data comprising a characteristic of a metabolite ”. Regarding the above cited limitations in claim 1 of ( i ) computer -readable memory storing executable instructions and time-series multiomics data of an organism ; and (ii) one or more hardware processors programmed by the executable instructions to perform steps . These limitations require only a generic computer component, which does not improve computer technology. Therefore, these limitations equate to mere instructions to implement an abstract idea on a generic computer, which the courts have established does not render an abstract idea eligible in Alice Corp. 573 U.S. at 223, 110 USPQ2d at 1983. Regarding the above cited limitations in claims 17 and 28 of (iii) receiving time-series multiomics data of a plurality of strains of an organism comprising time-series proteomics data of a plurality of proteins and time-series metabolomics data comprising a characteristic of a metabolite ; and (iv) receiving time-series multiomics data of a plurality of strains of an organism comprising time-series proteomics data of comprising a characteristic of each of a plurality of proteins and time-series metabolomics data comprising a characteristic of a metabolite . These limitations equate to insignificant, extra-solution activity of mere data gathering because these limitations gather data before or after the recited judicial exceptions of using a machine learning model to determine a characteristic of a metabolite in a virtual strain of an organism (see MPEP § 2106.04(d)). Additionally, none of the recited dependent claims recite additional elements which would integrate the judicial exception into a practical application. Specifically, claim 15 recites an extra-solution “apply it” step in designing a new strain without any steps of how the design is accomplished (see MPEP § 2106.05(f) ) and an extra-solution data gathering step analogous to claims 17 and 28 above; and claim 26 recites extra solution “apply it” steps of designing and/or creating a new strain without any steps of how the design and/or creation is accomplished (see MPEP § 2106.05(f) ) . As such, claims 1-10, 12-19, 26, and 28 are directed to an abstract idea ( Step 2A , Prong Two: NO ). Step 2B : Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself ( Step 2B ). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The instant independent claims recite the same additional elements described in Step 2A , Prong Two above. Regarding the above cited limitations in claim 1 of ( i ) computer -readable memory storing executable instructions and time-series multiomics data of an organism ; and (ii) one or more hardware processors programmed by the executable instructions to perform steps . These limitations equate to instructions to implement an abstract idea on a generic computing environment, which the courts have established does not provide an inventive concept (see MPEP § 2106.05(d) and MPEP § 2106.05(f)). Regarding the above cited limitations in claims 17 and 28 of (iii) receiving time-series multiomics data of a plurality of strains of an organism comprising time-series proteomics data of a plurality of proteins and time-series metabolomics data comprising a characteristic of a metabolite ; and (iv) receiving time-series multiomics data of a plurality of strains of an organism comprising time-series proteomics data of comprising a characteristic of each of a plurality of proteins and time-series metabolomics data comprising a characteristic of a metabolite . These limitations do not include any specific steps for generating or acquiring the time-series multiomics data. Under the BRI, these limitations are merely receiving data for the subsequent steps of training a machine learning model and using the machine learning model to predict a characteristic or property. Therefore, t hese limitations equate to receiving/transmitting data over a network, which the courts have establishe d as a WURC limitation of a generic computer in buySAFE , Inc. v. Google, Inc ., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014). These additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the instant claims do not amount to significantly more than the judicial exception itself ( Step 2B : NO ). As such, claims 1-10, 12-19, 26, and 28 are not patent eligible . Claim Rejections - 35 USC § 102 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis ( i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale , or otherwise available to the public before the effective filing date of the claimed invention. Claims 1-7, 9-10, 12-14, 16-19, and 28 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Costello et al. (A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data. NPJ Syst Biol Appl. 4(19): 1-14 (2018); published 5/29/2018; cited in the IDS dated 8/15/2023). Regarding claim 1, Costello et al. teaches a method of combining machine learning and multiomics data to predict pathway dynamics in bioengineered systems (i.e., a system for simulating a virtual strain of an organism ) (Abstract). Costello et al. further teaches that the data set comprises three proteomic and metabolic time-series (strains) from an isopentenol producing E. coli and three time-series (strains) from limonene producing E. coli (i.e., wherein the time-series multiomics data comprises time-series proteomics data of a plurality of proteins and time-series metabolomics data comprising a characteristic of a metabolite ) (Pg. 11, Col. 2, Para. 4). Costello et al. further teaches that the code to implement the steps of the method is provided in GitHub (i.e., on a generic computer comprising computer-readable memory storing executable instructions and time-series multiomics data of an organism and one or more hardware processors programmed by the executable instructions ) (Pg. 11, Col. 1, Para. 3). Costello et al. further teaches that in order to train a machine learning model, a suitable training set must be created. They expect the trained machine learning model to take in metabolite and protein concentrations at a particular point in time and return the derivative of the metabolite concentrations at the same time point. Once the training data set is established, a machine learning model must be selected to learn the relationship between input and outputs. The model selection process used a meta-learning package in python called TPOT . TPOT uses genetic algorithms to find a model with the best cross-validated performance on the training set. After TPOT determines the optimal models associated with each metabolite, they are trained on the data set of interest. Models with the lowest tenfold cross-validated prediction root mean squared error were selected. In this way, the best validated models were selected for use (i.e., training a machine learning model with the time-series proteomics data as input and the time-series metabolomics data of the metabolite as output ) (Pg. 11, Col. 1, Para. 4 – Col. 2, Para. 1). Costello et al. further teaches that once the models are trained, they can use them to predict metabolite concentrations (Pg. 11, Col. 2, Para. 3). Costello et al. further teaches that they predicted the pathway dynamics (i.e., metabolite concentrations as a function of time) from protein concentration data for two pathways of relevance to metabolic engineering and synthetic biology: a limonene producing pathway and an isopentenol producing pathway (i.e., simulating a virtual strain of the organism using the machine learning model to determine the characteristic of the metabolite in the virtual strain ) (Pg. 4, Col. 2, Para. 2). Regarding claim 2, Costello et al. teaches that there are three (high, medium, and low production) variants for strains which produce isopentenol and limonene, respectively. All strains were derived from E. coli DH1 . The low and high-producing strain for each pathway were used to predict the medium production strain dynamic (i.e., wherein the time-series multiomics data comprises time-series multiomics data of a plurality of strains of the organism ) (Pg. 11, Col. 2, Para. 5). Regarding claim 3, Costello et al. teaches that the method was tested on the limonene and isopentenol metabolic pathways. The limonene and isopentenol producing pathways are variants of the mevalonate pathway. Time-series proteomics and metabolomics data are used to learn the dynamics of both the isopentenol and limonene producing strains (i.e., wherein the time-series proteomics data is associated with a metabolic pathway ) (Pg. 4, Fig. 3). Regarding claim 5, Costello et al. teaches that proteomics and metabolomics data for two different heterologous pathways engineered into E. coli were available (i.e., wherein the metabolic pathway comprises a heterologous pathway ) (Pg. 11, Col. 2, Para. 5). Regarding claim 6, Costello et al. teaches an alternative to traditional kinetic modeling by using machine learning. The goal is to use time series proteomics data to predict time-series metabolomics data. The traditional approach involves using ordinary differential equations where the change in metabolites over time is given by Michaelis–Menten kinetics. The alternative approach proposed here uses time series of proteomics and metabolomics data to feed machine learning algorithms in order to predict pathway dynamics. While the machine learning approach necessitates more data, it can be automatically applied to any pathway or host, leverages systematically new data sets to improve accuracy, and captures dynamic relationships which are unknown by the literature or have a different dynamic form than Michaelis–Menten kinetics (i.e., wherein the machine learning model represents kinetics of the metabolic pathway ) (Pg. 2, Fig. 1). Regarding claim 7, Costello et al. teaches that limonene producing strains ( L1 , L2 , and L3 ) produce limonene from acetyl-CoA. L1 is the unoptimized strain with the naively chosen variants for each protein in the pathway. It is a two plasmid system where the lower and upper parts of the pathway are split between both constructs. L2 is a DH1 variant that contains the entire limonene pathway on a single plasmid. L3 is another two plasmid strain where the entire pathway is present on the first plasmid, and the terpene synthases are on a second plasmid for increased expression. Starting at induction, each strain had measurements taken at seven time points during fermentation over 72 h r . At each time point pathway, metabolite measurements and pathway protein measurements were collected (i.e., wherein the proteomics data comprises a concentration of each of a plurality of proteins at each of a plurality of time points, and wherein the metabolomics data comprises a concentration of the metabolite at each of the plurality of time points ) (Pg. 11, Col. 2, Para. 6). Regarding claim 9, Costello et al. teaches that they used the supervised learning method to predict pathway dynamics ( e.g. , metabolite concentrations as a function of time) from protein concentration data for two pathways of relevance to metabolic engineering and synthetic biology: a limonene producing pathway and an isopentenol producing pathway (i.e., wherein simulating the virtual strain of the organism comprises determining a concentration of the metabolite of the virtual strain using the machine learning model ) (Pg. 4, Col. 1, Para. 2). Regarding claim 10, Costello et al. teaches that t hey used TPOT to select the best pipelines it can find from the scikit-learn library combining 11 different regressors and 18 different preprocessing algorithms. This model selection process is done independently for each metabolite (Pg. 11, Col. 1, Para. 7 – Col. 2, Para. 1). The machine learning models for different metabolites are shown in Table S1 , and include random forest, decision trees, k neighbors regressor, etc . (i.e., wherein the machine learning model comprises a supervised machine learning model, a non-classification model, a neural network, a recurrent neural network ( RNN ), a linear regression model, a logistic regression model, a decision tree, a support vector machine, a Naive Bayes network, a k-nearest neighbors (KNN) model, a k-means model, a random forest model, a multilayer perceptron, or a combination thereof ) (Supplementary Information, Pg. 14, Table S1 ). Regarding claim 12, Costello et al. teaches that i n order to showcase how biological insights can be derived, they trained the ML model using 50 proteomics and metabolomics time series, using the Michaelis–Menten kinetic model as ground truth. Another 50 proteomics time series were held back as a test data set. Each metabolite time series was predicted using the machine learning model and the associated proteomic time series. The final time point proteomics and final production were collected for each predicted strain. The final time point proteomics data was plotted in two dimensions with a basis selected by performing a partial least squares regression between the proteomics and final production data. These first basis vector from a PLS regression is the direction that explains the most covariance between the proteomics data and production data (i.e., wherein the machine learning model comprises a partial least square (PLS) model ) (Pg. 12, Col. 1, Para. 7 – Col. 2, Para. 1). Regarding claim 13, Costello et al. teaches that the machine learning models for different metabolites are shown in Table S1 . Some metabolites use a combination of models, such as experimental limonene, which uses an Extra Trees Regressor and a Random Forest Regressor (i.e., wherein the machine learning model comprises an ensemble model of a plurality of machine learning models ) (Supplementary Information, Pg. 14, Table S1 ). Regarding claim 14, Costello et al. teaches that biological insights can be generated by using the machine learning model to produce data in substitution of bench experiments. For example, similarly to principal component analysis of proteomics ( PCAP68 ), we can use the simulations to determine which proteins to over express / underexpress , and for which base strain, in order to improve production. Proteins LS, AtoB , PMD , and Idi are the most important drivers of production in the case of limonene: changing protein expression along the principal component associated with them increases limonene creation ( i.e., wherein the virtual strain comprises an increased expression of at least one first protein, a knock-out of at least one second protein, a reduced expression of at least one third protein, or a combination thereof ) (Pg. 6, Col. 2, Para. 3 and Pg. 10, Fig. 10). Regarding claim 16, Costello et al. teaches that in the training set each time series contains seven data points. These are too sparse to formulate accurate models. To overcome this a data augmentation scheme is employed where seven time points from the original data are expanded into 200 for each strain. This is done by smoothing the data with a Savitzky-Golay filter and interpolating over the filtered curve ( i.e., wherein the one or more hardware processors are further programmed to perform: interpolating the timeseries multiomics data from a first number of time points to a second number of time points ) (Pg. 11, Col. 2, Para. 7). Regarding claim 17, Costello et al. teaches the limitations of receiving time-series multiomics data of a plurality of strains of an organism comprising time-series proteomics data of a plurality of proteins and time-series metabolomics data comprising a characteristic of a metabolite ; training a machine learning model with the time-series proteomics data as input and the time-series metabolomics data of the metabolite as output ; and simulating a virtual strain of the organism using the machine learning model to determine the characteristic of the metabolite in the virtual strain as described for claim 1 above. Regarding claim 18, Costello et al. teaches that in the training set each time series contains seven data points. These are too sparse to formulate accurate models. To overcome this a data augmentation scheme is employed where seven time points from the original data are expanded into 200 for each strain. This is done by smoothing the data with a Savitzky-Golay filter and interpolating over the filtered curve (i.e., wherein receiving the time-series multiomics data comprises preprocessing of the time-series multiomics data of the plurality of strains of the organism ) (Pg. 11, Col. 1, Para. 7). Regarding claim 19, Costello et al. teaches the use of metabolomics data for two different heterologous pathways engineered into E. coli . There are three (high, medium, and low production) variants for strains which produce isopentenol and limonene, respectively. All strains were derived from E. coli DH1 . The low and high-producing strain for each pathway were used to predict the medium production strain dynamics (i.e., wherein the time-series multiomics data comprises multiomics data of two or more time-series of a strain ) (Pg. 11, Col. 2, Para. 5). Regarding claim 28, Costello et al. teaches the limitations of receiving time-series multiomics data of a plurality of strains of an organism comprising time-series proteomics data of a plurality of proteins and time-series metabolomics data comprising a characteristic of a metabolite ; and training a machine learning model with the time-series proteomics data as input and the time-series metabolomics data of the metabolite as output as described for claim 1 above. Costello et al. further teaches that biological insights can be generated by using the machine learning model to produce data in substitution of bench experiments. For example, similarly to principal component analysis of proteomics ( PCAP68 ), we can use the simulations to determine which proteins to over express / underexpress , and for which base strain, in order to improve production . Proteins LS, AtoB , PMD , and Idi are the most important drivers of production in the case of limonene: changing protein expression along the principal component associated with them increases limonene creation (i.e., determining modifications of a concentration of each of one or more proteins using the machine learning model ) (Pg. 6, Col. 2, Para. 3 and Pg. 10, Fig. 10). Therefore, Costello et al. teaches all the limitations in claims 1-7, 9-10, 12-14, 16-19, and 28 . 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. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness . This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 1. Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Costello et al. as applied to claim s 1-7, 9-10, 12-14, 16-19, and 28 above, and further in view of Brunk et al. ( Characterizing Strain Variation in Engineered E. coli Using a Multi-Omics-Based Workflow. Cell Systems . 2 (5) : 335-346 (2016); published 5/25/2016 ; cited in the IDS dated 8/15/2023 ) . Costello et al. , as applied to claims 1-7, 9-10, 12-14, 16-19, and 28 above, does not teach wherein the multiomics data comprises triplicates of a concentration of a protein at a time point and triplicates of a concentration of the metabolite at a time point . Regarding claim 8, Brunk et al. teaches a workflow that integrates metabolomics, proteomics, and genome-scale models of Escherichia coli metabolism to study the effects of introducing a heterologous pathway into a microbial host (Abstract). Brunk et al. further teaches that metabolites and peptides were measured in triplicate (i.e., wherein the multiomics data comprises triplicates of a concentration of a protein at a time point and triplicates of a concentration of the metabolite at a time point ) (Supplemental Information, Pg. 4, Fig. S3 legend). Therefore, regarding claim 8 , 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 method of combining machine learning with multiomics data to predict pathway dynamics of Costello et al. with the teachings of Brunk et al. because the proteomics and metabolomics data used to train the model of Costello et al. were available from Brunk et al. (Costello et al., Pg. 11, Col. 2, Para. 5) . One of ordinary skill in the art would be able to combine the teachings of Costello et al. with Brunk et al. with reasonable expectation of success due to the same nature of the problem to be solved, since both are drawn towards a method for analysis of multiomics data . Therefore, regarding claim 8 , the instant invention is prima facie obvious (MPEP § 2142). 2. Claims 15 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Costello et al. as applied to claims 1-7, 9-10, 12-14, 16-19, and 28 above, and further in view of Vavricka et al. (Dynamic Metabolomics for Engineering Biology: Accelerating Learning Cycles for Bioproduction. Trends in Biotechnology . 38(1): 68-82 (2020); published 12/18/2019). Costello et al. , as applied to claims 1-7, 9-10, 12-14, 16-19, and 28 above , does not teach designing one or more new strains based on the virtual strain (claim 15); receiving experimental time-series multiomics data for the new strains (claim 15); retraining the machine learning model based on the experimental time-series multiomics data of the new strains (claim 15); and designing a strain of the organism corresponding to the virtual strain and/or creating a strain of the organism corresponding to the virtual strain (claim 26). Regarding claim 15 Vavricka et al. teaches a review on using metabolomics as a tool to rationally guide metabolic engineering of synthetic bioproduction pathways (Abstract). Vavricka et al. further teaches the Design, Build, Test, and Learn cycle for metabolomics. In the cycle, the machine learning algorithm is used to design new strains, followed by construction and evaluation of the strains (i.e., designing one or more new strains based on the virtual strain and receiving experimental time-series multiomics data for the new strains ). The results from the strain evaluation are fed back into the machine learning algorithm for subsequent design (i.e., retraining the machine learning model based on the experimental time-series multiomics data of the new strains ) (Pg. 72, Fig. 2). Regarding claim 26, Vavricka et al. teaches the Design, Build, Test, and Learn cycle for metabolomics as described for claim 15 above. In the cycle, the machine learning algorithm is used to design new strains, followed by construction and evaluation of the strains (i.e., designing a strain of the organism corresponding to the virtual strain and creating a strain of the organism corresponding to the virtual strain ) (Pg. 72, Fig. 2). Therefore, regarding claim s 15 and 26 , it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the method of combining machine learning with multiomics data to predict pathway dynamics of Costello et al. in the design, build, test, learn ( DBTL ) cycle of Vavricka et al. to improve the learning processes step of the DBTL cycle and expand bioproduction capability ( Vavricka et al., Abstract) . One of ordinary skill in the art would be able to combine the teachings of Costello et al. with Vavricka et al. w ith reasonable expectation of success due to the same nature of the problem to be solved, since both are drawn towards a method for using metabolomics for strain design . Therefore, regarding claim s 15 and 26 , the instant invention is prima facie obvious (MPEP § 2142). Double Patenting The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg , 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman , 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi , 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum , 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel , 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington , 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1 . For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA/25, or PTO/AIA/26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer . Claims 1-6 , 9-10, 15, 17, 19 , and 26 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1- 8, 11, 15-16 and 28-29 of U.S. Patent No. 11,636,917 B2 ( provided in the IDS dated 8/15/2023; herein the ‘917 patent). Although the claims at issue are not identical, they are not patentably distinct from each other because both the instant application and the ‘917 patent are drawn to a system/method for simulating a virtual strain of an organism, comprising steps of receiving time-series multiomics data, training a machine learning model with the time-series multiomics data , and simulating a virtual strain of an organism using the machine learning model to determine a characteristic of a metabolite . The characteristic can be a concentration of a metabolite (instant claims 1 and 17, and ‘917 patent claims 1/11 and 15). Conclusion No claims allowed. Inquiries Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT DIANA P SANFORD whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-6504 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Mon-Fri 8am-5pm EST . 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Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center ( EBC ) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /D.P.S./ Examiner, Art Unit 1687 /Karlheinz R. Skowronek/ Supervisory Patent Examiner, Art Unit 1687