diff --git a/benchmarks/linear_pathway.py b/benchmarks/linear_pathway.py
index a7aacad..c9a014c 100644
--- a/benchmarks/linear_pathway.py
+++ b/benchmarks/linear_pathway.py
@@ -6,7 +6,7 @@
 
 Reactions r1 and r3 behave according to the law of mass action, and reaction r2 according to the Michaelis Menten rate law. We assume we have measurements of Aint and Bint, as well as plenty of information about all the kinetic parameters and boundary conditions, and that the pathway is in a steady state, so that the concentrations c_m1_int and c_m2_int are not changing.
 
-To formulate this situation as a statistical modelling problem, there are two functions `rmm` and `ma` that specify rate laws, and another function `fn` that specifies a steady state problem, i.e. finding values for c_m1_int and c_m2_int that put the system in a steady state. 
+To formulate this situation as a statistical modelling problem, there are two functions `rmm` and `ma` that specify rate laws, and another function `fn` that specifies a steady state problem, i.e. finding values for c_m1_int and c_m2_int that put the system in a steady state.
 
 We can then specify joint and posterior log density functions in terms of log scale parameters, which we can sample using GrapeNUTS.