ADD a bunch of references

tex/references.bib

@@ -1180,6 +1180,226 @@ CONCLUSIONS: We developed a simplified, semi-closed system for the initial selec
   publisher = {Springer Science and Business Media {LLC}},
 }
 
+@Misc{purcellmain,
+  title     = {{Purcell Biolytica Main Page}},
+  timestamp = {2020-04-26},
+  url       = {http://www.percell.se/default.htm},
+}
+
+@InCollection{Kotancheka,
+  author    = {Mark Kotanchek and Guido Smits and Ekaterina Vladislavleva},
+  booktitle = {Genetic and Evolutionary Computation},
+  publisher = {Springer {US}},
+  title     = {Exploiting Trustable Models via Pareto {GP} for Targeted Data Collection},
+  year      = {2009},
+  pages     = {1--18},
+  doi       = {10.1007/978-0-387-87623-8_10},
+}
+
+@Article{Witkowska2005,
+  author    = {Anna Maria Witkowska},
+  journal   = {Mediators of Inflammation},
+  title     = {On the Role of {sIL}-2R Measurements in Rheumatoid Arthritis and Cancers},
+  year      = {2005},
+  number    = {3},
+  pages     = {121--130},
+  volume    = {2005},
+  doi       = {10.1155/mi.2005.121},
+  publisher = {Hindawi Limited},
+}
+
+@Article{Mehta2018,
+  author    = {Amit K. Mehta and Donald T. Gracias and Michael Croft},
+  journal   = {Cytokine},
+  title     = {{TNF} activity and T cells},
+  year      = {2018},
+  month     = {jan},
+  pages     = {14--18},
+  volume    = {101},
+  doi       = {10.1016/j.cyto.2016.08.003},
+  publisher = {Elsevier {BV}},
+}
+
+@Article{Vudattu2005,
+  author    = {Nalini K. Vudattu and Ernst Holler and Patricia Ewing and Ute Schulz and Silvia Haffner and Verena Burger and Silvia Kirchner and Reinhard Andreesen and Gunther Eissner},
+  journal   = {Immunology},
+  title     = {Reverse signalling of membrane-integrated tumour necrosis factor differentially regulates alloresponses of {CD}4+ and {CD}8+ T cells against human microvascular endothelial cells},
+  year      = {2005},
+  month     = {aug},
+  number    = {4},
+  pages     = {536--543},
+  volume    = {115},
+  doi       = {10.1111/j.1365-2567.2005.02190.x},
+  publisher = {Wiley},
+}
+
+@Article{Wong2011,
+  author    = {F. S. Wong},
+  journal   = {Diabetes},
+  title     = {Stimulating {IL}-13 Receptors on T cells: A New Pathway for Tolerance Induction in Diabetes?~},
+  year      = {2011},
+  month     = {may},
+  number    = {6},
+  pages     = {1657--1659},
+  volume    = {60},
+  doi       = {10.2337/db11-0353},
+  publisher = {American Diabetes Association},
+}
+
+@Article{Junttila2018,
+  author    = {Ilkka S. Junttila},
+  journal   = {Frontiers in Immunology},
+  title     = {Tuning the Cytokine Responses: An Update on Interleukin ({IL})-4 and {IL}-13 Receptor Complexes},
+  year      = {2018},
+  month     = {jun},
+  volume    = {9},
+  doi       = {10.3389/fimmu.2018.00888},
+  publisher = {Frontiers Media {SA}},
+}
+
+@Article{Amatya2017,
+  author    = {Nilesh Amatya and Abhishek V. Garg and Sarah L. Gaffen},
+  journal   = {Trends in Immunology},
+  title     = {{IL}-17 Signaling: The Yin and the Yang},
+  year      = {2017},
+  month     = {may},
+  number    = {5},
+  pages     = {310--322},
+  volume    = {38},
+  doi       = {10.1016/j.it.2017.01.006},
+  publisher = {Elsevier {BV}},
+}
+
+@Article{Becher2016,
+  author    = {Burkhard Becher and Sonia Tugues and Melanie Greter},
+  journal   = {Immunity},
+  title     = {{GM}-{CSF}: From Growth Factor to Central Mediator of Tissue Inflammation},
+  year      = {2016},
+  month     = {nov},
+  number    = {5},
+  pages     = {963--973},
+  volume    = {45},
+  doi       = {10.1016/j.immuni.2016.10.026},
+  publisher = {Elsevier {BV}},
+}
+
+@Article{Hurton2016,
+  author    = {Lenka V. Hurton and Harjeet Singh and Amer M. Najjar and Kirsten C. Switzer and Tiejuan Mi and Sourindra Maiti and Simon Olivares and Brian Rabinovich and Helen Huls and Marie-Andr{\'{e}}e Forget and Vrushali Datar and Partow Kebriaei and Dean A. Lee and Richard E. Champlin and Laurence J. N. Cooper},
+  journal   = {Proceedings of the National Academy of Sciences},
+  title     = {Tethered {IL}-15 augments antitumor activity and promotes a stem-cell memory subset in tumor-specific T cells},
+  year      = {2016},
+  month     = {nov},
+  number    = {48},
+  pages     = {E7788--E7797},
+  volume    = {113},
+  doi       = {10.1073/pnas.1610544113},
+  publisher = {Proceedings of the National Academy of Sciences},
+}
+
+@Article{RonHarel2016,
+  author    = {Noga Ron-Harel and Daniel Santos and Jonathan~M. Ghergurovich and Peter~T. Sage and Anita Reddy and Scott~B. Lovitch and Noah Dephoure and F.~Kyle Satterstrom and Michal Sheffer and Jessica~B. Spinelli and Steven Gygi and Joshua~D. Rabinowitz and Arlene~H. Sharpe and Marcia~C. Haigis},
+  journal   = {Cell Metabolism},
+  title     = {Mitochondrial Biogenesis and Proteome Remodeling Promote One-Carbon Metabolism for T Cell Activation},
+  year      = {2016},
+  month     = {jul},
+  number    = {1},
+  pages     = {104--117},
+  volume    = {24},
+  doi       = {10.1016/j.cmet.2016.06.007},
+  publisher = {Elsevier {BV}},
+}
+
+@Article{Pietzke2020,
+  author    = {Matthias Pietzke and Johannes Meiser and Alexei Vazquez},
+  journal   = {Molecular Metabolism},
+  title     = {Formate metabolism in health and disease},
+  year      = {2020},
+  month     = {mar},
+  pages     = {23--37},
+  volume    = {33},
+  doi       = {10.1016/j.molmet.2019.05.012},
+  publisher = {Elsevier {BV}},
+}
+
+@Article{Vardhana2020,
+  author    = {Santosha A. Vardhana and Madeline A. Hwee and Mirela Berisa and Daniel K. Wells and Kathryn E. Yost and Bryan King and Melody Smith and Pamela S. Herrera and Howard Y. Chang and Ansuman T. Satpathy and Marcel R. M. van den Brink and Justin R. Cross and Craig B. Thompson},
+  journal   = {Nature Immunology},
+  title     = {Impaired mitochondrial oxidative phosphorylation limits the self-renewal of T cells exposed to persistent antigen},
+  year      = {2020},
+  month     = {jul},
+  number    = {9},
+  pages     = {1022--1033},
+  volume    = {21},
+  doi       = {10.1038/s41590-020-0725-2},
+  publisher = {Springer Science and Business Media {LLC}},
+}
+
+@Article{Lunt2011,
+  author    = {Sophia Y. Lunt and Matthew G. Vander Heiden},
+  journal   = {Annual Review of Cell and Developmental Biology},
+  title     = {Aerobic Glycolysis: Meeting the Metabolic Requirements of Cell Proliferation},
+  year      = {2011},
+  month     = {nov},
+  number    = {1},
+  pages     = {441--464},
+  volume    = {27},
+  doi       = {10.1146/annurev-cellbio-092910-154237},
+  publisher = {Annual Reviews},
+}
+
+@Article{Chang2013,
+  author    = {Chih-Hao Chang and Jonathan~D. Curtis and Leonard~B. Maggi and Brandon Faubert and Alejandro~V. Villarino and David O'Sullivan and Stanley~Ching-Cheng Huang and Gerritje~J.W. van~der~Windt and Julianna Blagih and Jing Qiu and Jason~D. Weber and Edward~J. Pearce and Russell~G. Jones and Erika~L. Pearce},
+  journal   = {Cell},
+  title     = {Posttranscriptional Control of T Cell Effector Function by Aerobic Glycolysis},
+  year      = {2013},
+  month     = {jun},
+  number    = {6},
+  pages     = {1239--1251},
+  volume    = {153},
+  doi       = {10.1016/j.cell.2013.05.016},
+  publisher = {Elsevier {BV}},
+}
+
+@Article{Cao2014,
+  author    = {Yilin Cao and Jeffrey C. Rathmell and Andrew N. Macintyre},
+  journal   = {{PLoS} {ONE}},
+  title     = {Metabolic Reprogramming towards Aerobic Glycolysis Correlates with Greater Proliferative Ability and Resistance to Metabolic Inhibition in {CD}8 versus {CD}4 T Cells},
+  year      = {2014},
+  month     = {aug},
+  number    = {8},
+  pages     = {e104104},
+  volume    = {9},
+  doi       = {10.1371/journal.pone.0104104},
+  editor    = {Michael Platten},
+  publisher = {Public Library of Science ({PLoS})},
+}
+
+@Article{Almeida2016,
+  author    = {Lu{\'{\i}}s Almeida and Matthias Lochner and Luciana Berod and Tim Sparwasser},
+  journal   = {Seminars in Immunology},
+  title     = {Metabolic pathways in T cell activation and lineage differentiation},
+  year      = {2016},
+  month     = {oct},
+  number    = {5},
+  pages     = {514--524},
+  volume    = {28},
+  doi       = {10.1016/j.smim.2016.10.009},
+  publisher = {Elsevier {BV}},
+}
+
+@Article{Wang_2012,
+  author    = {Ruoning Wang and Douglas R Green},
+  journal   = {Nature Immunology},
+  title     = {Metabolic checkpoints in activated T cells},
+  year      = {2012},
+  month     = {sep},
+  number    = {10},
+  pages     = {907--915},
+  volume    = {13},
+  doi       = {10.1038/ni.2386},
+  publisher = {Springer Science and Business Media {LLC}},
+}
+
 @Comment{jabref-meta: databaseType:bibtex;}
 
 @Comment{jabref-meta: grouping:

tex/thesis.tex

@@ -1873,31 +1873,34 @@ We have developed a T cell expansion system that recapitulates key features of
 the in vivo lymph node microenvironment using DMSs functionalized with
 activating mAbs. This strategy provided superior expansion with higher number of
 naïve/memory and CD4+ T cells compared to state-of-the-art microbead technology
-(Figure 2). Other groups have used biomaterials approaches to mimic the in vivo
+(Figure 2). Other groups have used biomaterials approaches to mimic the \invivo{}
-microenvironment13–15,17,34; however, to our knowledge this is the first system
+microenvironment\cite{Cheung2018, Rio2018, Delalat2017, Lambert2017, Matic2013};
-that specifically drives naïve/memory and CD4+ T cell formation in a scalable,
+however, to our knowledge this is the first system that specifically drives
-potentially bioreactor-compatible manufacturing process.
+naïve/memory and CD4+ T cell formation in a scalable, potentially
+bioreactor-compatible manufacturing process.
 
 Memory and naïve T cells have been shown to be important clinically. Compared to
 effectors, they have a higher proliferative capacity and are able to engraft for
 months; thus they are able to provide long-term immunity with smaller
-doses19,35. Indeed, less differentiated T cells have led to greater survival
+doses\cite{Gattinoni2012, Joshi2008}. Indeed, less differentiated T cells have
-both in mouse tumor models and human patients20,36,37. Furthermore, clinical
+led to greater survival both in mouse tumor models and human
+patients\cite{Fraietta2018, Adachi2018, Rosenberg2011}. Furthermore, clinical
 response rates have been positively correlated with T cell expansion, implying
 that highly-proliferative naïve and memory T cells are a significant
-contributor18,38. Circulating memory T cells have also been found in complete
+contributor\cite{Xu2014, Besser2010}. Circulating memory T cells have also been
-responders who received CAR T cell therapy39.
+found in complete responders who received CAR T cell therapy\cite{Kalos2011}.
 
 Similarly, CD4 T cells have been shown to play an important role in CAR T cell
 immunotherapy. It has been shown that CAR T doses with only CD4 or a mix of CD4
-and CD8 T cells confer greater tumor cytotoxicity than only CD8 T cells22,40.
+and CD8 T cells confer greater tumor cytotoxicity than only CD8 T
-There are several possible reasons for these observations. First, CD4 T cells
+cells\cite{Wang2018, Sommermeyer2015}. There are several possible reasons for
-secrete proinflammatory cytokines upon stimulation which may have a synergistic
+these observations. First, CD4 T cells secrete proinflammatory cytokines upon
-effect on CD8 T cells. Second, CD4 T cells may be less prone to exhaustion and
+stimulation which may have a synergistic effect on CD8 T cells. Second, CD4 T
-may more readily adopt a memory phenotype compared to CD8 T cells22. Third, CD8
+cells may be less prone to exhaustion and may more readily adopt a memory
-T cells may be more susceptible than CD4 T cells to dual stimulation via the CAR
+phenotype compared to CD8 T cells\cite{Wang2018}. Third, CD8 T cells may be more
-and endogenous T Cell Receptor (TCR), which could lead to overstimulation,
+susceptible than CD4 T cells to dual stimulation via the CAR and endogenous T
-exhaustion, and apoptosis23. Despite evidence for the importance of CD4 T cells,
+Cell Receptor (TCR), which could lead to overstimulation, exhaustion, and
+apoptosis\cite{Yang2017}. Despite evidence for the importance of CD4 T cells,
 more work is required to determine the precise ratios of CD4 and CD8 T cell
 subsets to be included in CAR T cell therapy given a disease state.
 
@@ -1957,25 +1960,25 @@ approach are that the DMSs are large enough to be filtered (approximately 300
 that DMSs are also desired, digestion with dispase or collagenase may be used.
 Collagenase D may be selective enough to dissolve the DMSs yet preserve surface
 markers which may be important to measure as critical quality attributes CQAs
-{Figure X}. Furthermore, our system should be compatible with
+{Figure X}. Furthermore, our system should be compatible with large-scale static
-large-scale static culture systems such as the G-Rex bioreactor or perfusion
+culture systems such as the G-Rex bioreactor or perfusion culture systems, which
-culture systems, which have been previously shown to work well for T cell
+have been previously shown to work well for T cell expansion\cite{Forget2014,
-expansion12,50,51. The microcarriers used to create the DMSs also have a
+  Gerdemann2011, Jin2012}. The microcarriers used to create the DMSs also have a
 regulatory history in human cell therapies that will aid in clinical
 translation.; they are already a component in an approved retinal pigment
 epithelial cell product for Parkinson’s patients, and are widely available in 30
-countries26.
+countries\cite{purcellmain}.
 
 It is important to note that all T cell cultures in this study were performed up
 to 14 days. Others have demonstrated that potent memory T cells may be obtained
-simply by culturing T cells as little as 5 days using traditional beads30. It is
+simply by culturing T cells as little as 5 days using traditional
-unknown if the naïve/memory phenotype of our DMS system could be further
+beads\cite{Ghassemi2018}. It is unknown if the naïve/memory phenotype of our DMS
-improved by reducing the culture time, but we can hypothesize that similar
+system could be further improved by reducing the culture time, but we can
-results would be observed given the lower number of doublings in a 5 day
+hypothesize that similar results would be observed given the lower number of
-culture. We should also note that we investigated one subtype (\ptmem{}) in
+doublings in a 5 day culture. We should also note that we investigated one
-this study. Future work will focus on other memory subtypes such as tissue
+subtype (\ptmem{}) in this study. Future work will focus on other memory
-resident memory and stem memory T cells, as well as the impact of using the DMS
+subtypes such as tissue resident memory and stem memory T cells, as well as the
-system on the generation of these subtypes.
+impact of using the DMS system on the generation of these subtypes.
 
 % TODO this sounds sketchy
 Another advantage is that the DMS system appears to induce a faster growth rate
@@ -1989,10 +1992,10 @@ the allogeneic T cell model would greatly benefit from a system that could
 create large numbers of T cells with naïve and memory phenotype. In contrast to
 the autologous model which is currently used for Kymriah and Yescarta,
 allogeneic T cell therapy would reduce cost by spreading manufacturing expenses
-across many doses for multiple patients52. Since it is economically advantageous
+across many doses for multiple patients\cite{Harrison2019}. Since it is
-to grow as many T cells as possible in one batch in the allogeneic model
+economically advantageous to grow as many T cells as possible in one batch in
-(reduced start up and harvesting costs, fewer required cell donations), the DMSs
+the allogeneic model (reduced start up and harvesting costs, fewer required cell
-offer an advantage over current technology.
+donations), the DMSs offer an advantage over current technology.
 
 % TODO this is already stated in the innovation section
 It should be noted that while we demonstrate a method providing superior
@@ -2009,13 +2012,14 @@ cells, this method can theoretically be applied to any T cell immunotherapy
 which responds to anti-CD3/CD28 mAb and cytokine stimulation. These include
 \glspl{til}, virus-specific T cells (VSTs), T cells engineered to express
 $\upgamma\updelta$TCR (TEGs), $\upgamma\updelta$ T cells, T cells with
-transduced-TCR, and CAR-TCR T cells53–58. Similar to CD19-CARs used in liquid
+transduced-TCR, and CAR-TCR T cells\cite{Cho2015, Straetemans2018, Robbins2011,
+  Brimnes2012, Baldan2015, Walseng2017}. Similar to CD19-CARs used in liquid
 tumors, these T cell immunotherapies would similarly benefit from the increased
 proliferative capacity, metabolic fitness, migration, and engraftment potential
-characteristic of naïve and memory phenotypes59–61. Indeed, since these T cell
+characteristic of naïve and memory phenotypes\cite{Blanc2018, Lalor2016,
-immunotherapies are activated and expanded with either soluble mAbs or
+  Rosato2019}. Indeed, since these T cell immunotherapies are activated and
-bead-immobilized mAbs, our system will likely serve as a drop-in substitution to
+expanded with either soluble mAbs or bead-immobilized mAbs, our system will
-provide these benefits.
+likely serve as a drop-in substitution to provide these benefits.
 
 \chapter{aim 2a}\label{aim2a}
 
@@ -2647,19 +2651,19 @@ industries12. SR discovers mathematical expressions that fit a given sample and
 differs from conventional regression techniques in that a model structure is not
 defined a priori13. Hence, a key advantage of this methodology is that
 transparent, human-interpretable models can be generated from small and large
-datasets with no prior assumptions14,15.
+datasets with no prior assumptions\cite{Kotancheka}.
 
 Since the model search process lets the data determine the model, diverse and
 competitive (e.g., accuracy, complexity) model structures are typically
 discovered. An ensemble of diverse models can be formed where its constituent
 models will tend to agree when constrained by observed data yet diverge in new
 regions. Collecting data in these regions helps to ensure that the target system
-is accurately modeled, and its optimum is accurately located14,15. Exploiting
+is accurately modeled, and its optimum is accurately located\cite{Kotancheka}.
-these features allows adaptive data collection and interactive modeling.
+Exploiting these features allows adaptive data collection and interactive
-Consequently, this adaptive-DOE approach is useful in a variety of scenarios,
+modeling. Consequently, this adaptive-DOE approach is useful in a variety of
-including maximizing model validity for model-based decision making, optimizing
+scenarios, including maximizing model validity for model-based decision making,
-processing parameters to maximize target yields, and developing emulators for
+optimizing processing parameters to maximize target yields, and developing
-online optimization and human understanding14,15.
+emulators for online optimization and human understanding\cite{Kotancheka}.
 
 % predictive features
 
@@ -2680,34 +2684,35 @@ exact ratio of expected cytokine abundance is less clear and depends on the
 subtypes present, and thus examination of each relevant cytokine is needed.
 
 IL2R is secreted by activated T cells and binds to IL2, acting as a sink to
-dampen its effect on T cells16. Since IL2R was much greater than IL2 in
+dampen its effect on T cells\cite{Witkowska2005}. Since IL2R was much greater
-solution, this might reduce the overall effect of IL2, which could be further
+than IL2 in solution, this might reduce the overall effect of IL2, which could
-investigated by blocking IL2R with an antibody. In T cells, TNF can increase
+be further investigated by blocking IL2R with an antibody. In T cells, TNF can
-IL2R, proliferation, and cytokine production18. It may also induce apoptosis
+increase IL2R, proliferation, and cytokine production\cite{Mehta2018}. It may
-depending on concentration and alter the CD4+ to CD8+ ratio17. Given that TNF
+also induce apoptosis depending on concentration and alter the CD4+ to CD8+
-has both a soluble and membrane-bound form, this may either increase or decrease
+ratio\cite{Vudattu2005}. Given that TNF has both a soluble and membrane-bound
-CD4+ ratio and/or memory T cells depending on the ratio of the membrane to
+form, this may either increase or decrease CD4+ ratio and/or memory T cells
-soluble TNF18. Since only soluble TNF was measured, membrane TNF is needed to
+depending on the ratio of the membrane to soluble TNF\cite{Mehta2018}. Since
-understand its impact on both CD4+ ratio and memory T cells. Furthermore, IL13
+only soluble TNF was measured, membrane TNF is needed to understand its impact
-is known to be critical for Th2 response and therefore could be secreted if
+on both CD4+ ratio and memory T cells. Furthermore, IL13 is known to be critical
-there are significant Th2 T cells already present in the starting population19.
+for Th2 response and therefore could be secreted if there are significant Th2 T
-This cytokine has limited signaling in T cells and is thought to be more of an
+cells already present in the starting population\cite{Wong2011}. This cytokine
-effector than a differentiation cytokine20. It might be emerging as relevant due
+has limited signaling in T cells and is thought to be more of an effector than a
-to an initially large number of Th2 cells or because Th2 cells were
+differentiation cytokine\cite{Junttila2018}. It might be emerging as relevant
+due to an initially large number of Th2 cells or because Th2 cells were
 preferentially expanded; indeed, IL4, also found important, is the conical
 cytokine that induces Th2 cell differentiation (Fig.3). The role of these
 cytokines could be investigated by quantifying the Th1/2/17 subsets both in the
 starting population and longitudinally. Similar to IL13, IL17 is an effector
-cytokine produced by Th17 cells21 thus may reflect the number of Th17 subset of
+cytokine produced by Th17 cells\cite{Amatya2017} thus may reflect the number of
-T cells. GM-CSF has been linked with activated T cells, specifically Th17 cells,
+Th17 subset of T cells. GM-CSF has been linked with activated T cells,
-but it is not clear if this cytokine is inducing differential expansion of CD8+
+specifically Th17 cells, but it is not clear if this cytokine is inducing
-T cells or if it is simply a covariate with another cytokine inducing this
+differential expansion of CD8+ T cells or if it is simply a covariate with
-expansion22. Finally, IL15 has been shown to be essential for memory signaling
+another cytokine inducing this expansion\cite{Becher2016}. Finally, IL15 has
-and effective in skewing CAR-T cells toward the Tscm phenotype when using
+been shown to be essential for memory signaling and effective in skewing CAR-T
-membrane-bound IL15Ra and IL15R23. Its high predictive behavior goes with its
+cells toward the Tscm phenotype when using membrane-bound IL15Ra and
-ability to induce large numbers of memory T cells by functioning in an
+IL15R\cite{Hurton2016}. Its high predictive behavior goes with its ability to
-autocrine/paracrine manner and could be explored by blocking either the cytokine
+induce large numbers of memory T cells by functioning in an autocrine/paracrine
-or its receptor.
+manner and could be explored by blocking either the cytokine or its receptor.
 
 Moreover, many predictive metabolites found here are consistent with metabolic
 activity associated with T cell activation and differentiation, yet it is not
@@ -2715,23 +2720,25 @@ clear how the various combinations of metabolites relate with each other in a
 heterogeneous cell population. Formate and lactate were found to be highly
 predictive and observed to positively correlate with higher values of total live
 CD4+ TN+TCM cells (Fig.5a-b;Supp.Fig.28-S30,S38). Formate is a byproduct of the
-one-carbon cycle implicated in promoting T cell activation24. Importantly, this
+one-carbon cycle implicated in promoting T cell activation\cite{RonHarel2016}.
-cycle occurs between the cytosol and mitochondria of cells and formate
+Importantly, this cycle occurs between the cytosol and mitochondria of cells and
-excreted25. Mitochondrial biogenesis and function are shown necessary for memory
+formate excreted\cite{Pietzke2020}. Mitochondrial biogenesis and function are
-cell persistence26,27. Therefore, increased formate in media could be an
+shown necessary for memory cell persistence\cite{van_der_Windt_2012,
-indicator of one-carbon metabolism and mitochondrial activity in the culture.
+  Vardhana2020}. Therefore, increased formate in media could be an indicator of
+one-carbon metabolism and mitochondrial activity in the culture.
 
 In addition to formate, lactate was found as a putative CQA of TN+TCM. Lactate
 is the end-product of aerobic glycolysis, characteristic of highly proliferating
-cells and activated T cells28,29. Glucose import and glycolytic genes are
+cells and activated T cells\cite{Lunt2011, Chang2013}. Glucose import and
-immediately upregulated in response to T cell stimulation, and thus generation
+glycolytic genes are immediately upregulated in response to T cell stimulation,
-of lactate. At earlier time-points, this abundance suggests a more robust
+and thus generation of lactate. At earlier time-points, this abundance suggests
-induction of glycolysis and higher overall T cell proliferation. Interestingly,
+a more robust induction of glycolysis and higher overall T cell proliferation.
-our models indicate that higher lactate predicts higher CD4+, both in total and
+Interestingly, our models indicate that higher lactate predicts higher CD4+,
-in proportion to CD8+, seemingly contrary to previous studies showing that CD8+
+both in total and in proportion to CD8+, seemingly contrary to previous studies
-T cells rely more on glycolysis for proliferation following activation30. It may
+showing that CD8+ T cells rely more on glycolysis for proliferation following
-be that glycolytic cells dominate in the culture at the early time points used
+activation\cite{Cao2014}. It may be that glycolytic cells dominate in the
-for prediction, and higher lactate reflects more cells.
+culture at the early time points used for prediction, and higher lactate
+reflects more cells.
 
 % TODO not sure how much I should include here since I didn't do this analysis
 % AT ALL
@@ -2753,18 +2760,18 @@ primary carbon source (glucose) and essential amino acids (BCAA, histidine) that
 must be continually consumed by proliferating cells. Moreover, the inclusion of
 glutamine in our predictive models also suggests the importance of other carbon
 sources for certain T cell subpopulations. Glutamine can be used for oxidative
-energy metabolism in T cells without the need for glycolysis30. Overall, these
+energy metabolism in T cells without the need for glycolysis\cite{Cao2014}.
-results are consistent with existing literature that show different T cell
+Overall, these results are consistent with existing literature that show
-subtypes require different relative levels of glycolytic and oxidative energy
+different T cell subtypes require different relative levels of glycolytic and
-metabolism to sustain the biosynthetic and signaling needs of their respective
+oxidative energy metabolism to sustain the biosynthetic and signaling needs of
-phenotypes33,34. It is worth noting that the trends of metabolite abundance here
+their respective phenotypes\cite{Almeida2016,Wang_2012}. It is worth noting that
-are potentially confounded by the partial replacement of media that occurred
+the trends of metabolite abundance here are potentially confounded by the
-periodically during expansion (Methods), thus likely diluting some metabolic
+partial replacement of media that occurred periodically during expansion
-byproducts (i.e. formate, lactate) and elevating depleted precursors (i.e.
+(Methods), thus likely diluting some metabolic byproducts (i.e. formate,
-glucose, amino acids). More definitive conclusions of metabolic activity across
+lactate) and elevating depleted precursors (i.e. glucose, amino acids). More
-the expanding cell population can be addressed by a closed system, ideally with
+definitive conclusions of metabolic activity across the expanding cell
-on-line process sensors and controls for formate, lactate, along with ethanol
+population can be addressed by a closed system, ideally with on-line process
-and glucose.
+sensors and controls for formate, lactate, along with ethanol and glucose.
 
 \chapter{aim 2b}\label{aim2b}