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Supplementary 1: Material and methods Determination of CHO cell composition For the measurement of cell composition, five different CHO cell lines (CHO mAb M250-9, M500-7, CHO K1, CHO DG44 and CHO DXB11) were used. CHO mAb M250-9, M500-7 and CHO K1(ATCC No. CCL-61) cell lines were cultured in a mixture consisting of a 1:1 (v/v) ratio of HyQ PF-CHO (Hyclone, UT, USA) and CD CHO (Gibco-Invitrogen, CA, USA) supplemented with 6 mM L-glutamine (Sigma Chemical Co., MO, USA) and 0.05% (v/v) Pluronic F-68 (Invitrogen, CA, USA). Additional 250 nM and 500 nM MTX (Sigma Chemical Co., MO, USA) were added to the M250-9 and M500-7 cultures respectively. CHO DG44 and CHO DXB11(ATCC No. CRL-9096) cell lines were cultured in HyQ PF-CHO (Hyclone, Logan, UT) supplemented with 4 mM L-glutamine (Sigma Chemical Co., MO, USA), 0.1% (v/v) Pluronic F-68 (Invitrogen, CA, USA) and 1× HT supplement (Gibco). All the cells were grown in their respective media under batch conditions. Cell pellets (total mass ranging from 19 g to 38 g) were collected between the exponential to stationary phase of the various cultures, when viabilities were > 90% (CHO K1, CHO DG44 and CHO DXB11 on day 4, and CHO mAb M250-9 and M500-7 on day 7). The samples were analyzed for 19 amino acids and 36 fatty acids; as well as moisture content using AOAC certified methods (Pacific Lab Services, Singapore). From the moisture content, dry cell weight equivalents of the various analytes could be calculated. 1 Metabolic model development and in silico analysis Recently available genome-scale metabolic model for mammalian cells accounts for 1494 metabolic reactions, 724 ORFs and 715 enzymes (Selvarasu et al. 2010). We expanded the network model by adding reactions obtained from CHO cDNA annotation resulting in the addition of 38 new reactions from various metabolic subsystems (see supplementary 1 for list of reactions). We also examined the model for missing links and improved the network connectivity by filling the gaps with necessary reactions. Concurrently, those reactions that were not found in CHO were removed from the model. As a result, a metabolic model with 1540 metabolic reactions was obtained. Once a stoichiometrically balance metabolic network is developed based on the metabolites and reactions, the steady state metabolic flux distributions across the network can be quantitatively predicted by resorting to constraints-based flux analysis. Often, cell growth phenotype is maximized while satisfying thermodynamic and physico-chemical constraints using the following linear programming (LP) model (Edwards and Palsson 1998). N Min/max Z c j v j (1) j 1 N s. t. S v j 1 ij j j vj j 0 i = 1, 2, …, M (2) j = 1, 2, …, N (3) 2 where, i refers to metabolite, j refers to reaction, Z is the desired cellular objective, vj (αj ≤ vj ≤ βj) is the flux of reaction j with bounds αj and βj, for an irreversible reaction j, αj = 0 and βj = ∞, and for a reversible reaction αj = –∞ and βj = ∞. cj is the weight associated with the reaction fluxes represented in objective function, and Sij is the stoichiometric coefficient of metabolite i in reaction j. M is the number of metabolites and N is the number of reactions in the model. When simulating the non-growth condition, maximizing cell growth cannot be chosen as cellular objective. Therefore, we utilized least square minimization technique to reduce the sum square error between experimentally measured fluxes and in silico simulation fluxes. This resulted in NLP problem as described below. N in Min Z vexp j vj 2 (4) j 1 where, vjexp represent experimentally measured uptake/secretion rates of metabolites and vjin correspond to uptake/secretion flux values obtained from in silico simulation. The resulting optimization problem can be solved with eqn. 4 as objective with eqn. 2 and eqn. 3 as constraints using GAMS software (Brooke et al. 1998). PCA and PLS analysis A linear multivariate PCA technique was used to analyze the cell culture nutrients data. Initially, experimental data were collected in a data matrix possessing the dimension [n x p] with n number of measurements and p observations (cell density, concentration of amino acids, major carbon sources and byproducts). A set of orthogonal eigen vectors [1 x p] was estimated from 3 the data matrix based on the eigen values that indicate the total variance in the data. From the list of eigen vectors, a small set known as principal components (key variables) can be determined, representing vast majority of variance in the data. Subsequently, we used PLS analysis to predict a set of dependent variables (e.g., cell growth, antibody production, lactate and NH3 secretion) from a large set of independent variables (predictors) such as consumption of amino acids and glucose. PLS regression generalizes and combines features from PCA and multiple regressions (Geladi and Kowalski 1986). It finds components from independent variables (X) that are relevant to dependant variables (Y), thus describing their common structures. Specifically, it searches for components (called latent vectors) that perform a simultaneous decomposition of X and Y with the inclusion of a constraint. This constraint determines the latent vectors that can explain the varying trend of X and Y as closely as possible. It is then followed by a regression step where the decomposition of X is used to predict Y. All the analyses were conducted using MATLAB platform. References Brooke A, Kendrick D, Meeraus A, Raman R. 1998. GAMS: a user’s guide. GAMS development corporation. Edwards JS, Palsson BO. 1998. How will bioinformatics influence metabolic engineering? Biotechnology and Bioengineering 58(2-3):162-169. Geladi P, Kowalski BR. 1986. Partial least square regression: a tutorial. Anal Chim Acta 185:1-17. Selvarasu S, Karimi IA, Ghim GH, Lee DY. 2010. Genome-scale modeling and in silico analysis of mouse cell metabolic network. Molecular Biosystems 6(1):142-151. 4