LoopDetectR: Comprehensive Feedback Loop Detection in ODE models
Installation
LoopDetectR is on CRAN and can be installed within R by
Alternatively, you can install the package from gitlab. Call the following commands in an R session.
# Install the package including the vignette and the manual
remotes::install_gitlab("kabaum/LoopDetectR", build_manual=TRUE,
build_vignettes = TRUE)After installation, load the package.
In brief and quick start
The package LoopDetectR enables determining all feedback loops of an ordinary differential equation (ODE) system at user-defined values of the model parameters and of the modelled variables.
The following call reports (up to 10) feedback loops for an ODE
system determined by a function, here the example function
func_POSm4, at variable values s_star (here,
these are all equal to 1). Additional arguments to the example function
are supplied.
# Load example ODE system with function func_POSm4, 4 variables
data("func_POSm4")
# Example variable values
s_star <- rep(1,4)
# Further arguments of func_POSm4, in addition: time t as argument
klin <- rep(1,8)
knonlin <- c(2.5,3)
# compute loops
res_tab <- find_loops_vset(func_POSm4,vset=list(s_star),t=1,klin=klin,
knonlin=knonlin,max_num_loops=10)
# The loop list is reported
res_tab$loop_rep[[1]]## loop length sign
## 1 1, 1 1 -1
## 2 2, 2 1 -1
## 3 3, 3 1 -1
## 4 4, 4 1 -1
## 5 3, 4, 1,.... 4 -1
## 6 3, 4, 2, 3 3 1# This is the sixth loop of the list. It is a positive feedback loop (sign in
# the loop list equals +1) of length 3 in that variable 3 regulates variable 4,
# variable 4 regulates variable 2, and variable 2 regulates variable 3.
res_tab$loop_rep[[1]][6,]## loop length sign
## 6 3, 4, 2, 3 3 1## [,1] [,2] [,3] [,4]
## [1,] -1 0 0 -1
## [2,] 1 -1 0 1
## [3,] 0 1 -1 0
## [4,] 0 0 1 -1Introduction
Ordinary differential equation (ODE) models are used frequently to mathematically represent biological systems. Feedback loops are important regulatory features of biological systems and can give rise to different dynamic behavior such as multistability for positive feedback loops or oscillations for negative feedback loops.
The feedback loops in an ODE system can be detected with the help of its Jacobian matrix, the matrix of partial derivatives of the variables. It captures all interactions between the variables and gives rise to the interaction graph of the ODE model. In this graph, each modelled variable is a node and non-zero entries in the Jacobian matrix are (weighted) edges of the graph. Interactions can be positive or negative, according to the sign of the Jacobian matrix entry.
Directed path detection in this graph is used to determine all feedback loops (in graphs also called cycles or circuits) of the system. They are marked by a set of directed interactions forming a chain in which only the first and the last node (variable) is the same. Thereby, self-loops (loops of length one) can also occur.
LoopDetectR allows for detection of all loops of the graph and also reports the sign of each loop, i.e. whether it is a positive feedback loop (the number of negative interactions is even) or a negative feedback loop (the number of negative interactions is uneven). The output is a table that captures the order of the variables forming the loop, their length and the sign of each loop.
Jacobian determination in LoopDetectR relies on the package
numDeriv, and path finding in graphs uses algorithms
supplied in the package igraph.
Solving the ODE model to generate variable values of interest
Solving an ODE model can be performed with the package
deSolve. Note: You can skip this step if you already have a
point of interest in state space, or if you want to use dummy values for
the variables such as s_star <- 1:4.
# Load example ODE system with function func_POSm4,
# Positive feedback chain model from [Baum et al., 2016], 4 variables
data(func_POSm4)
# The function func_POSm4 returns a vector, but deSolve needs the vector within
# a list as output. Therefore, we define a function that simply puts the output
# of func_POSm4 into a list:
func_POSm4_list <- function(t,x,klin,knonlin){list(func_POSm4(t,x,klin,knonlin))}
# Kinetic parameters of the model, supplied as arguments to func_POSm4
klin <- c(165,0.044,0.27,550,5000,78,4.4,5.1)
knonlin <- c(0.3,2)
# Solve the system using deSolve
sol <- deSolve::ode(y = rep(1,4), times = seq(0,15,0.1), func = func_POSm4_list,
parms=klin, knonlin=knonlin)
# The solution of the 4-variable system is oscillatory, showing only the first
# variable here
plot(sol[,1],sol[,2],type='l',xlab='time',ylab ='variable 1')
# Set the last point of the numeric solution as point of interest, omit the
# first column (it contains the time)
s_star <- sol[dim(sol)[1],2:dim(sol)[2]]State variable values of interest could be steady state values, values at a specific point in time (e.g. after a stimulus) or even a set of values (see section Determining loops over a set of variable values).
Calculating the Jacobian matrix
The function jacobian from the numDeriv
package can be used to determine numerically the Jacobian matrix of an
ODE system at a certain set of values for the variables,
s_star. The approach is that of finite differences (with
real step) or complex step approach, the latter of which is supposed to
deliver more exact results [Martins et al., 2003].
The input function, in the example func_POSm4 (positive
feedback chain model from [Baum et al., 2016]) defines the time
derivatives of the modelled variables as a vector: fi(s) = dSi/dt.
Note that only those input arguments to the function that encode the
modelled variables (and hence in whose direction the partial derivatives
are taken) are allowed to be called x.
klin <- c(165,0.044,0.27,550,5000,78,4.4,5.1)
knonlin <- c(0.3,2)
j_matrix <- numDeriv::jacobian(func_POSm4,s_star,method="complex",
t=1,klin=klin,knonlin=knonlin,)
j_matrix## [,1] [,2] [,3] [,4]
## [1,] -1.2396132 0 0.0 -85.9719
## [2,] 0.9696132 -5550 0.0 85.9719
## [3,] 0.0000000 550 -82.4 0.0000
## [4,] 0.0000000 0 78.0 -5.1000The (i,j)th entry of the Jacobian matrix denotes the partial
derivative of variable Si with respect
to variable Sj, Jij = δSi/δSj,
which is positive if Sj has a direct
positive effect on Si, negative if
Sj has a
direct negative effect on Si and zero if
Sj does
not have a direct effect on Si. For example,
the entry in row 2, column 4, J24, of the
signed_jacobian matrix above is positive, meaning that in
the underlying ODE model, variable 4 positively regulates variable
1.
Computing all feedback loops and useful functions for loop search
The Jacobian matrix is used to compute feedback loops in the
generated interaction graph. The default function for this is
find_loops, in that strongly connected components are
determined to reduce runtime. For smaller systems, the function
find_loops_noscc skips this step and thus can be faster.
The optional second input argument, max_num_loops, sets an
upper limit to the number of detected and reported loops and thus can
prevent overly long runtime (but also potentially not all loops are
returned).
## loop length sign
## 1 1, 1 1 -1
## 2 2, 2 1 -1
## 3 3, 3 1 -1
## 4 4, 4 1 -1
## 5 3, 4, 1,.... 4 -1
## 6 3, 4, 2, 3 3 1# The signed Jacobian matrix can be supplied instead, delivering the same results
loop_list <- find_loops(signed_jacobian)
loop_list## loop length sign
## 1 1, 1 1 -1
## 2 2, 2 1 -1
## 3 3, 3 1 -1
## 4 4, 4 1 -1
## 5 3, 4, 1,.... 4 -1
## 6 3, 4, 2, 3 3 1The output loop list is an R data.frame with one row for each
detected loop. The column loop contains the order of the
variables that form the loop (as vector in a list); length
contains the loop length (i.e. the number of variables involved);
sign denotes whether the loop is negative, -1,
or positive, 1. The data.frame can be queried as usual for
single loops or loops of a certain length or sign.
## [[1]]
## [1] 3 4 1 2 3# In order to obtain the vector of the order of variables of a loop, you have to
# call the list element
loop_list[5,1][[1]]## [1] 3 4 1 2 3## loop length sign
## 5 3, 4, 1,.... 4 -1The LoopDetectR function loop_summary provides a
convenient report on total number of loops, subdivided by their lengths
(len_i) and signs (all, pos,
neg).
## len_1 len_2 len_3 len_4
## all 4 0 1 1
## pos 0 0 1 0
## neg 4 0 0 1One can filter the loop list for loops containing specific variables, for example the one with index 2:
# Index of node of interest
noi <- 2
# Return all loops from loop_list containing node 2
loop_list[vapply(loop_list$loop,function(x){noi %in% x},logical(1)),]## loop length sign
## 2 2, 2 1 -1
## 5 3, 4, 1,.... 4 -1
## 6 3, 4, 2, 3 3 1The LoopDetectR function find_edge can be used to search
a loop list for loops containing specific edges defined by the indices
of the ingoing and outgoing nodes. This example returns the indices of
all loops with a regulation of node 3 by node 2. These are only two
here.
# Obtain the indices of the loops with edge '2 regulates 3' in loop_list
loop_edge_ind <- find_edge(loop_list,source_node=2,target_node=3);
loops_with_edge_2_to_3 <- loop_list[loop_edge_ind,]
loops_with_edge_2_to_3## loop length sign
## 5 3, 4, 1,.... 4 -1
## 6 3, 4, 2, 3 3 1Loop lists can be saved and loaded as R objects using the usual save
(or saveRDS) and load (loadRDS) R functions. Saving the loop list into
table files is possible, but the vectors containing the loops are saved
as string "c(x,y,z)" and need special handling when loading
back into R or into other programming language frameworks.
# This writes a file 'looplist_func_POSm4.RData'
save(loop_list,file=file.path(tempdir(),'looplist_func_POSm4.RData'))
# This loads the object 'loop_list' into the workspace
load(file.path(tempdir(),'looplist_func_POSm4.RData'))
# This saves the loop list into a table with separation by tab
write.table(loop_list,file=file.path(tempdir(),'looplist_func_POSm4.txt'),
sep='\t',quote=F,row.names=F)
# This reads the loop list back into the R session, as object ll
ll <- read.table(file.path(tempdir(),'looplist_func_POSm4.txt'),
header=T,sep='\t')
ll## loop length sign
## 1 c(1, 1) 1 -1
## 2 c(2, 2) 1 -1
## 3 c(3, 3) 1 -1
## 4 c(4, 4) 1 -1
## 5 c(3, 4, 1, 2, 3) 4 -1
## 6 c(3, 4, 2, 3) 3 1# This transforms the "c(x,y,z)"-entries into the format used by LoopDetectR
ll$loop <- lapply(ll$loop,function(x){eval(parse(text=x))})
ll## loop length sign
## 1 1, 1 1 -1
## 2 2, 2 1 -1
## 3 3, 3 1 -1
## 4 4, 4 1 -1
## 5 3, 4, 1, 2, 3 4 -1
## 6 3, 4, 2, 3 3 1Computing feedback loops over multiple sets of variable values of interest
In this example of a model of the bacterial cell cycle [Li et al., 2008], it is demonstrated how feedback loops can be determined over multiple sets of variable values. Here, it is focused on the solution of the ODE systems along the time axis (provided as data in the package, li08_solution.RData).
# Load in the example ODE model function func_li08, the kinetic parameters are
# defined within the function, and the function returns a vector of the time
# derivatives (in the same order as the modelled variables in the arguments)
data("func_li08")
# Load sets of variable values (solution to the ODE over time)
data("li08_solution") #loads the data.frame li08_solution, columns: variables
# Cast the solution into the correct list format and remove time (first column)
li08_sol_list <- as.list(as.data.frame(t(li08_solution[,-1])))
# Compute all different loop lists along the solution
res_tab <- find_loops_vset(func_li08,vset=li08_sol_list,t=1,
compute_full_list=FALSE)The solutions of the example ODE model give rise to seven different
loop lists that are saved as elements of the list
res_tab$loop_rep. Here, two examples of resulting loop
lists are given (without self-loops).
## loop length sign
## 16 6, 7, 1, 6 3 -1
## 17 6, 7, 6 2 1
## 18 2, 3, 2 2 -1## loop length sign
## 15 1, 2, 1 2 -1
## 16 1, 3, 2, 1 3 1
## 17 2, 3, 2 2 -1
## 18 5, 7, 1,.... 4 -1
## 19 6, 7, 1,.... 5 1
## 20 6, 7, 1, 6 3 -1
## 21 6, 7, 6 2 1
## 22 10, 11, .... 6 1
## 23 10, 12, .... 3 -1
## 24 10, 13, .... 4 -1
## 25 10, 14, .... 8 -1
## 26 10, 14, .... 7 1The entry res_tab$loop_rep_index is a vector of the same
length as the number of different states at which the loops were
determined (length(li08_sol_list)), and returns which entry
of res_tab$loop_rep belongs to each input state.
# Determine the loop list belonging to the 10th up to 20th input state.
# It is loop_list_2 for all of these input states.
res_tab$loop_rep_index[10:20]## [1] 2 2 2 2 2 2 2 2 2 2 2Similarly, res_tab$jac_rep and
res_tab$jac_rep_index capture the different Jacobian
matrices and for each input state which Jacobian belongs to it.
Results of this analysis could be plotted along the solution and
analyzed further to discover reasons of changing loops. Please note that
in order to obtain the sample solution in li08_solution.RData
also event functions are required; the solution cannot be retrieved from
integrating func_li08 alone. Please refer to the model’s
publication [Li et al., 2008] for details.
Comparing two loop lists
LoopDetectR provides a function for comparing the loops of two
systems, compare_loop_list. For a meaningful comparison,
the loop indices in the compared systems should point to the same
variables. This could be the case when regulations change within one
system between different sets of variables of interest (along a dynamic
trajectory, at different steady states of the system), or when comparing
different systems in which one or more regulations are altered (as in
the below example the positive feedback chain model vs. the negative
feedback chain model, [Baum et al., 2016]).
# Load the ODE function of the positive feedback chain model
data(func_POSm4)
# Set kinetic parameters
klin <- c(165,0.044,0.27,550,5000,78,4.4,5.1)
knonlin <- c(0.3,2)
# Compute the Jacobian matrix of the system at the state [1,1,1,1]
j_matrix <- numDeriv::jacobian(func_POSm4,rep(1,4),method="complex",
t=1, klin=klin, knonlin=knonlin)
# Compute all loops for this Jacobian
loop_list_pos <- find_loops(j_matrix)
# Function with negative regulation. The altered regulation affects
# two entries of the Jacobian matrix. Parameter values and the set of
# variable values remain identical.
j_matrix_neg <- sign(j_matrix)
j_matrix_neg[1:2,4] <- -sign(j_matrix[1:2,4])
# Compute the loop list for this Jacobian with altered regulation
loop_list_neg <- find_loops(j_matrix_neg);
# Compute comparison
comp_inds <- compare_loop_list(loop_list_pos,loop_list_neg)
# Only the four self-loops remain identical in both systems (saved in ind_a_id).
loop_list_pos[comp_inds$ind_a_id,]## loop length sign
## 1 1, 1 1 -1
## 2 2, 2 1 -1
## 3 3, 3 1 -1
## 4 4, 4 1 -1# ind_b_id saves the indices of the corresponding loops in the negatively
# regulated system.
loop_list_neg[comp_inds$ind_b_id,]## loop length sign
## 1 1, 1 1 -1
## 2 2, 2 1 -1
## 3 3, 3 1 -1
## 4 4, 4 1 -1# Two loops are the same in both systems but they have switched their signs.
# Their indices in the first list are saved in ind_a_switch
loop_list_pos[comp_inds$ind_a_switch,]## loop length sign
## 5 3, 4, 1,.... 4 -1
## 6 3, 4, 2, 3 3 1## loop length sign
## 5 3, 4, 1,.... 4 1
## 6 3, 4, 2, 3 3 -1# All loops in the positively regulated system do also occur in the negatively
# regulated system, i.e. ind_a_notin is empty.
comp_inds$ind_a_notin## integer(0)References
Baum K, Politi AZ, Kofahl B, Steuer R, Wolf J. Feedback, Mass Conservation and Reaction Kinetics Impact the Robustness of Cellular Oscillations. PLoS Comput Biol. 2016;12(12):e1005298.
Li S, Brazhnik P, Sobral B, Tyson JJ. A Quantitative Study of the Division Cycle of Caulobacter crescentus Stalked Cells. Plos Comput Biol. 2008;4(1):e9.
Martins JRRA, Sturdza P, Alonso JJ. The complex-step derivative approximation. ACM Trans Math Softw. 2003;29(3):245–62.
- Installation
- In brief and quick start
- Introduction
- Solving the ODE model to generate variable values of interest
- Calculating the Jacobian matrix
- Computing all feedback loops and useful functions for loop search
- Computing feedback loops over multiple sets of variable values of interest
- Comparing two loop lists
- References