Add better links

docs/src/examples/09_loopless.jl

@@ -2,10 +2,11 @@
 
 # Here we will use [`flux_balance_analysis`](@ref) and
 # [`flux_variability_analysis`](@ref) to analyze a toy model of *E. coli* that
-# is constrained in a way that removes all thermodynamically infeasible loops in the flux solution.
-# For more details about the algorithm, see Schellenberger, Lewis, and, Palsson. "Elimination
-# of thermodynamically infeasible loops in steady-state metabolic models.", *Biophysical
-# journal*, 2011 (https://doi.org/10.1016/j.bpj.2010.12.3707).
+# is constrained in a way that removes all thermodynamically infeasible loops in
+# the flux solution. For more details about the algorithm, see [Schellenberger,
+# and, Palsson., "Elimination of thermodynamically infeasible loops in
+# steady-state metabolic models.", Biophysical Journal,
+# 2011](https://doi.org/10.1016/j.bpj.2010.12.3707).
 
 # If it is not already present, download the model:
 

docs/src/examples/10_crowding.jl

@@ -4,11 +4,10 @@
 # the toy *E. coli* model that additionally respects common protein crowding
 # constraints. In particular, the model is limited by the amount of protein
 # required to run certain reactions. If that data is available, the predictions
-# are accordingly more realistic. See Beg, Qasim K., et al. "Intracellular
-# crowding defines the mode and sequence of substrate uptake by Escherichia coli
-# and constrains its metabolic activity." *Proceedings of the National Academy
-# of Sciences*, 104.31, 2007, (https://doi.org/10.1073/pnas.0609845104) for more
-# details.
+# are accordingly more realistic. See [Beg, et al., "Intracellular crowding
+# defines the mode and sequence of substrate uptake by Escherichia coli and
+# constrains its metabolic activity.", Proceedings of the National Academy of
+# Sciences,2007](https://doi.org/10.1073/pnas.0609845104) for more details.
 #
 # As usual, the same model modification can be transparently used with many
 # other analysis functions, including [`flux_variability_analysis`](@ref) and

docs/src/examples/12_mmdf.jl

@@ -4,10 +4,10 @@
 # Here, we use the max-min driving force analysis (MMDF) to find optimal
 # concentrations for the metabolites in glycolysis to ensure that the smallest
 # driving force across all the reactions in the model is as large as possible.
-# The method is described in more detail by Flamholz, Avi, et al., in
-# "Glycolytic strategy as a tradeoff between energy yield and protein cost.",
-# Proceedings of the National Academy of Sciences 110.24, 2013, 10039-10044
-# (https://doi.org/10.1073/pnas.1215283110).
+# The method is described in more detail by [Flamholz, et al., "Glycolytic
+# strategy as a tradeoff between energy yield and protein cost.", Proceedings of
+# the National Academy of Sciences,
+# 2013](https://doi.org/10.1073/pnas.1215283110).
 
 # We start as usual, with loading models and packages:
 

docs/src/examples/13_moma.jl

@@ -6,10 +6,9 @@
 # as a gene knockout) that prevents it from metabolizing optimally, but the
 # rest of the metabolism has not yet adjusted to compensate for the change.
 
-# The original description of MOMA is by: Segre, D., Vitkup, D., & Church, G. M.
-# (2002). Analysis of optimality in natural and perturbed metabolic networks.
-# *Proceedings of the National Academy of Sciences*, 99(23), 15112-15117
-# (https://doi.org/10.1073/pnas.232349399).
+# The original description of MOMA is by [Segre, Vitkup, and Church, "Analysis
+# of optimality in natural and perturbed metabolic networks", Proceedings of the
+# National Academy of Sciences, 2002](https://doi.org/10.1073/pnas.232349399).
 
 # As always, let's start with downloading a model.
 

docs/src/examples/14_smoment.jl

@@ -4,10 +4,9 @@
 # the cell to respect known enzymatic parameters and enzyme mass constraints
 # measured by proteomics and other methods.
 #
-# The original description from sMOMENT is by: Bekiaris, P.S. and Klamt, S., (2020).
-# "Automatic construction of metabolic models with enzyme constraints.",
-# *BMC bioinformatics*, 21(1), pp.1-13.
-# (https://doi.org/10.1186/s12859-019-3329-9)
+# The original description from sMOMENT is by [Bekiaris, and Klamt, "Automatic
+# construction of metabolic models with enzyme constraints.", BMC
+# bioinformatics, 2020](https://doi.org/10.1186/s12859-019-3329-9)
 #
 # Let's load some packages:
 

docs/src/examples/15_gecko.jl

@@ -1,14 +1,13 @@
 # # GECKO
 
-# GECKO algorithm can be used to easily adjust the metabolic activity within
-# the cell to respect many known parameters, measured by proteomics and other
+# GECKO algorithm can be used to easily adjust the metabolic activity within the
+# cell to respect many known parameters, measured by proteomics and other
 # methods.
 #
-# The original description from GECKO is by: Sánchez, B.J., Zhang, C., Nilsson,
-# A., Lahtvee, P.J., Kerkhoven, E.J. and Nielsen, J., (2017). "Improving the
-# phenotype predictions of a yeast genome‐scale metabolic model by
-# incorporating enzymatic constraints." *Molecular systems biology*, 13(8),
-# p.935 (https://doi.org/10.15252/msb.20167411).
+# The original description from GECKO is by: [Sánchez, et. al., "Improving the
+# phenotype predictions of a yeast genome‐scale metabolic model by incorporating
+# enzymatic constraints.", Molecular systems biology,
+# 2017](https://doi.org/10.15252/msb.20167411).
 #
 # The analysis method and implementation in COBREXA is similar to
 # [sMOMENT](14_smoment.md), but GECKO is able to process and represent much