 Research
 Open Access
Evaluating a common semimechanistic mathematical model of generegulatory networks
 Alexandru Mizeranschi^{1}Email author,
 Huiru Zheng^{2},
 Paul Thompson^{1} and
 Werner Dubitzky^{1}
https://doi.org/10.1186/175205099S5S2
© Mizeranschi et al. 2015
 Published: 1 September 2015
Abstract
Modeling and simulation of generegulatory networks (GRNs) has become an important aspect of modern systems biology investigations into mechanisms underlying gene regulation. A key challenge in this area is the automated inference (reverseengineering) of dynamic, mechanistic GRN models from gene expression timecourse data. Common mathematical formalisms for representing such models capture two aspects simultaneously within a single parameter: (1) Whether or not a gene is regulated, and if so, the type of regulator (activator or repressor), and (2) the strength of influence of the regulator (if any) on the target or effector gene. To accommodate both roles, "generous" boundaries or limits for possible values of this parameter are commonly allowed in the reverseengineering process. This approach has several important drawbacks. First, in the absence of good guidelines, there is no consensus on what limits are reasonable. Second, because the limits may vary greatly among different reverseengineering experiments, the concrete values obtained for the models may differ considerably, and thus it is difficult to compare models. Third, if high values are chosen as limits, the search space of the model inference process becomes very large, adding unnecessary computational load to the already complex reverseengineering process. In this study, we demonstrate that restricting the limits to the [−1, +1] interval is sufficient to represent the essential features of GRN systems and offers a reduction of the search space without loss of quality in the resulting models. To show this, we have carried out reverseengineering studies on data generated from artificial and experimentally determined from real GRN systems.
Keywords
 Modeling and simulation
 Generegulatory networks
 Automated model inference
Introduction
Systems biology refers to the quantitative analysis of the dynamic interactions among multiple components of a biological system and aims to understand the characteristics of a system as a whole [1, 2]. It involves the development and application of systemtheoretic concepts for the study of complex biological systems through iteration over mathematical modeling, computational simulation and biological experimentation. The regulation of genes and their products is at the heart of a systems view of complex biological processes. Hence, the modeling and simulation of generegulation networks (GRNs) is becoming an area of growing interest in systems biology research [3]. For instance, understanding generegulatory processes in the context of diseases is increasingly important for therapeutic development. Cells regulate the expression of their genes to create functional gene products (RNA, proteins) from the information stored in genes (DNA). Gene regulation is a complex process involving the transcription of genetic information from DNA to RNA, the translation of RNA information to make protein, and the posttranslational modification of proteins. Gene regulation is essential for life as it allows an organism to respond to changes in the environment by making the required amount of the right type of protein when needed. Developing quantitative models of gene regulation is essential to guide our understanding of complex generegulatory processes and systems. The approach considered in this study concentrates on a conceptualization of GRNs that ignores intricate intermediate biological processes of cellular gene regulation, such as splicing, capping, translation, binding and unbinding [4].
As the amount of gene expression data is growing, researchers are becoming increasingly interested in the automated inference or reverseengineering of quantitative dynamic, mechanistic generegulatory network models from gene expression timecourse data [5, 6, 4, 1, 7–9]. The quality of such reverseengineered GRN models is determined mainly by two factors:
The quality of a GRN model depends on two factors: the model's explanatory power (or model completeness) and the model's predictive power (or model correctness).
A model's explanatory power depends on how well the elements of a mathematical model specification correspond to the salient features of the modelled system. Thus, the explanatory power depends crucially on the concrete form of the chosen mathematical model. This is a decision made by the modeler at the start of a modeling process, hence it is related to the modeling error. A poor choice at this stage means low explanatory power and high modeling error. Even if an adequate model form is chosen, we can still end up with a model that has a low explanatory power. This can happen if we create a model with parameter values that are unrealistic, i.e. are not in good agreement with the corresponding system features.
A model's predictive power is estimated by simulating the system's response to the initial condition captured in an independent validation dataset [10]. The greater the deviation (error) between the response time courses predicted by the model and the actual time courses in the validation data, the lower the predictive power of the model.
The quality of the model inference or model reverseengineering algorithm is referred to as inferential power. The inferential power depends on the quality of the inferred GRN model. The higher the quality of the inferred model (in terms of explanatory and predictive power), the higher the inferential power of the algorithm. Reverseengineering of GRN models from data is a highly computeintensive process, hence another crucial aspect in deciding the quality of a reverseengineering algorithm is the computational performance of its implementation.

A lack of sufficient amounts of gene expression timecourse data. While the number of sampling points is important, far more important is to have multiple stimulusresponse data sets from the same system [5]. This is a challenging requirement for current experimental practice.

A lack of reverseengineering algorithms and methods that are able to incorporate existing biological knowledge effectively.
The main contribution of this study is to provide insight into the behavior of the structure parameter of commonly used GRN model formalisms and guidelines on how to deal with this parameter in similar optimizationbased reverseengineering procedures. Thus, the contribution of this study is not about a new method for GRN model inference, but a better understanding of the characteristics of existing formalisms in the context of automated GRN model inference procedures. We believe this is an important contribution, as it will help scientists to understand better the relationship between formalisms used to represent GRN models and automated procedures that generate such models from gene expression data.
The remainder of this paper is organized as follows: We first present two common semimechanistic mathematical models used to represent GRN systems, and an algorithm to automatically infer (reverseengineer) such models from geneexpression timecourse data. We then describe the main hypothesis underlying this study, the data (synthetic and biological) we used, and the basic experimental design and setup of the computational experiments we performed. This is followed by a section presenting the results of the experiments and their discussion and interpretation. First, we present and discuss training and validation errors obtained from the 192 GRN models derived from the 24 training data sets generated from 3 synthetic 5gene GRN systems. Then we present and discuss the training/validation errors from the 11gene GRN models we inferred from a yeast data set. Finally, in the Conclusions section, we reflect on the results of this study in the broader context of inferring reliable GRN models from timeseries gene expression data.
Methods
Rate law
The main assumption behind automated GRN model inference from timecourse gene expression data is that such data contains sufficient information to generate models that capture the essential mechanistic characteristics of the underlying biological GRN system. A common strategy for modeling and simulating dynamic GRNs is based on nonlinear ordinary differential equations (ODEs) that are derived from standard massbalance kinetic rate laws [2]. The ODEs in a GRN model relate changes in gene transcripts concentration to each other (and possibly to an external perturbations). Such models consist of one ODE for each gene in the GRN, where each equation describes the transcription rate of the gene as a function of the other genes (and of the external perturbations). The parameters of the equations have to be inferred from the expression timecourse data. ODE GRN models are similar to metabolic models that are formulated based on enzyme kinetics, where each rate law approximates a series of elementary chemical steps. Here, the rate laws are one level of complexity above that and represent a series of enzymatic steps. Because these rate laws combine mechanistic details into a small set of model parameters, they are sometimes referred to as "lumped" or "semimechanistic" models. In a sense, these models are neither fully mechanistic nor purely phenomenological.
 x_{ i }, x_{ j } ∈ {x_{1}, x_{2}, ..., x_{ n }}: Timedependent transcript concentration of gene i and j, respectively, where n is the total number of genes in the GRN system;
 dx_{ i }/dt ∈ ℝ: Total rate of x_{ i } change at time t;
 ${\widehat{\alpha}}_{i}\in {\mathbb{R}}^{+}:$Maximal synthesis rate of transcript x_{ i };
 ω_{ ij } ∈ ℝ: Type of synthesis regulation of transcript x_{ i } by x_{ j }, such that
ω_{ ij } >0: Synthesis activation of x_{ i } by x_{ j };
ω_{ ij } <0: Synthesis repression of x_{ i } by x_{ j };
ω_{ ij } = 0: No synthesis regulation of x_{ i } by x_{ j }.
$\left{\omega}_{ij}\right\phantom{\rule{2.36043pt}{0ex}}\in \phantom{\rule{2.36043pt}{0ex}}{\mathbb{R}}_{0}^{+}:$Relative weight of synthesisregulatory influence of x_{ j } on x_{ i };
 γ_{ i }: Activation/repression coefficient of gene i; γ_{ i } ∈ ℝ for ANN, and
γ_{ i } ∈ ℝ^{+} for Hill;
 n_{ ij } ∈ ℝ^{+}: Hill coefficient controlling the steepness of the sigmoidal regulation function; and
 β_{ i } ∈ ℝ^{+}: Degradation rate constant modulating the degradation rate of x_{ i }.
Both rate laws have been shown to represent essential characteristics of biological processes [2, 8, 12–15]. They capture a maximal synthesis rate (${\widehat{\alpha}}_{i}$), sigmoidal (saturable) kinetics, and an activation/repression threshold (γ_{ i }). For nij <1, the Hill rate law represents MichaelisMenten kinetics. The rate law versions shown in Eqs. 1 and 2 assume additive input processing and a linear transcript degradation rate (β_{ i }x_{ i }) that depends only on the concentration of the target gene's product. These assumptions are not set in stone; the rate laws may be adapted to capture multiplicative input processing and a nonlinear degradation rate which may depend on various influences. Variations that capture basal transcript synthesis and input delays are also possible [8, 2].
Like in other comparable GRN rate laws (e.g. the synergisticsystem [16]), the omega parameter (ω_{ ij }) represents two distinct biological concepts simultaneously; a discrete as well as a continuous concept. On one hand, it defines the nature or type of synthesis regulation between two genes i and j: if ω_{ ij } >0, then gene j activates synthesis of transcript x_{ i }, if ω_{ ij } <0, then gene j represses x_{ i } synthesis, and if ω_{ ij } = 0, then gene j does not regulate transcript x_{ i } at all. Hence, the totality of all ω_{ ij } parameters determines the transcript synthesisregulatory network structure of the GRN. On the other hand, the quantity ω_{ ij }  defines the strength or influence of a regulator gene j on its target or effector gene i. When we employ automated reverseengineering of GRN models from timecourse gene expression data with algorithms like the one illustrated in Algorithm 1, the dual role of ω_{ ij } and its discretecontinuous interpretation has important consequences.
First, because ω_{ ij } needs to be coded as a real number (ω_{ ij } ∈ ℝ), the chances of a typical parameter estimation algorithm to find ω_{ ij } = 0 are very small. Thus, reverseengineering algorithms like the one discussed below have a tendency to infer only nonzero values for ω_{ ij } , representing fully connected network structures. Fully connected GRN network structures are at best very difficult to interpret biologically, at worst meaningless.
Second, because typical GRN model formalisms like the ones in Eqs. 1 and 2 contain additional parameters to represent other quantitative aspects of GRN systems, reverseengineering algorithms tend to "balance" the quantitative values of all parameters. This means that only the relative quantities ω_{ ij }  are important, not their absolute values! It is important to understand this property, as this lies at the heart of this study.
Third, in the absence of a clear understanding of the effect ω_{ ij } has in the inference process, there is a danger that modelers choose large omega intervals in their algorithms. This, of course, adds additional computational load because it increases the size of the search or solution space.
Inference algorithm
Once one has chosen a rate law or model formalism to represent a GRN, one needs to determine the concrete values of the model's parameters  the parameters that describe the network structure, and the parameters that represent other aspects of the modeled GRN system. If these parameters are not known, they are typically inferred by reverseengineering or parameter estimation algorithms like the one defined by Algorithm 1.
Given stimulusresponse gene expression timecourse data, D, and a particular model formulation, M, Algorithm 1 determines concrete parameter values. The algorithm iterates over three main steps:
1. An optimizer algorithm that generates candidate model parameter values by attempting to minimize the training error, E.
2. An ODE solver component that numerically integrates the model equations using the initial values of the time series in the training data set, D.
Input: M ← Model equations; L ← Parameter limits; G ← Network topology; D ← Training data; ε ← Error threshold
Output: P ← Parameter values; E ← Training error;
(* Initialize and process: *)
S ← Simulation data (* Initialize *)
E ← ∞ (* Initialize *)
repeat
P ← Optimize(L, E) (* Parameter values *)
S ← SolveODE(M, P, D) (* Solve model *)
E ← Error(S, D) (* Determine error *)
until E < ε ;
Algorithm 1: Basic reverseengineering algorithm. The network topology, G, is an optional input. In this study, we experiment with known network topology only.
3. A component that computes the simulation error, E, based on the gene expression timecourse data in the training data set, D, and the predicted or simulated data, S, determined by the ODE solver.
The simulation of the predicted time series depicted in Figure 2 involves the numeric integration of the model equations. In terms of computational effort, the ODE solver step accounts for approximately for 80% of the total computing time of Algorithm 1. The reverseengineering process terminates, when the training error drops below a predefined error threshold, or when a maximum number of model evaluations is reached.
For this study we have employed the GRN modeling and simulation tool MultGrain/MAPPER. The tool was developed as a part of the European FP7 project Multiscale Applications on European eInfrastructures (MAPPER) [17]. The goal of MAPPER was to develop a general framework and technology facilitating the development, deployment and execution of distributed multiscale modeling and simulation applications [18, 19]. Based on tools and services developed in the MAPPER project, MultiGrain/MAPPER realizes the GRN model reverseengineering process (Figure 2) based on a multiswarm particle swarm optimization algorithm.
In order to estimate the model parameters, we used the our own implementation of a multiswarm particle swarm optimization [20] (PSO) algorithm. PSO is a populationbased metaheuristic inspired by the flocking, schooling or swarming behavior of animals. Two main advantages of this method include that it optimizes continuous variables and it has the ability to avoid getting stuck in local minima by using a multiswarm approach which successively swaps particles across each swarm after a fixed number of iterations in order to increase the "genetic" diversity of the overall swarm. The PSO parameters were set according to the guidelines of Pedersen et al. [21], who performed a metaanalysis of the PSO algorithm, testing its performance for a wide range of parameter values.
Hypothesis, data and experiments
The "fitness landscape" that the reverseengineering Algorithm 1 is allowed to explore is defined by the value ranges of the model parameter intervals. The basic meaningful ranges of the GRN model parameters in Eqs. 1 and 2 are specified below the equations. In order to limit the computational effort required to estimate the parameters, practical value ranges are typically much smaller than those shown.
In this study we have tested the following hypothesis: ω_{ ij } ∈ [−1, +1] is a sufficiently large permissible range for the important ω_{ ij } parameter values, because it is expressive enough
1. To encode the three regulatory interaction possibilities (synthesis activation, synthesis repression, no synthesis regulation) between two genes i and j, and
2. To represent the strength of the regulatory influence of gene j on i. As we have discussed, only the relative values of ω_{ ij }  are relevant, because of the way the ω_{ ij } parameters interact with one another and the other model parameters of the model Eqs. 1 and 2.
To test this hypothesis, we have conducted a number of experiments on data obtained from artificial and real GRN systems.
Training/validation data sets for each artificial systems A, B and C, with four different ω_{ ij } intervals.
ANN training data  ANN validation data  

ω−value  A  B  C  A  B  C 
{1, 0, +1}  T(A1,ANN)  T(B1,ANN)  T(C1,ANN)  V(A1,ANN)  V(B1,ANN)  V(C1,ANN) 
{5, 0, +5}  T(A5,ANN)  T(B5,ANN)  T(C5,ANN)  V(A5,ANN)  V(B5,ANN)  V(C5,ANN) 
{10, 0, +10}  T(A10,ANN)  T(B10,ANN)  T(C10,ANN)  V(A10,ANN)  V(B10,ANN)  V(C10,ANN) 
{20, 0, +20}  T(A20,ANN)  T(B20,ANN)  T(C20,ANN)  V(A20,ANN)  V(B20,ANN)  V(C20,ANN) 
Hill training data  Hill validation data  
A  B  C  A  B  C  
{1, 0, +1}  T(A1,Hill)  T(B1,Hill)  T(C1,Hill)  V(A1,Hill)  V(B1,Hill)  V(C1,Hill) 
{5, 0, +5}  T(A5,Hill)  T(B5,Hill)  T(C5,Hill)  V(A5,Hill)  V(B5,Hill)  V(C5,Hill) 
{10, 0, +10}  T(A10,Hill)  T(B10,Hill)  T(C10,Hill)  V(A10,Hill)  V(B10,Hill)  V(C10,Hill) 
{20, 0, +20}  T(A20,Hill)  T(B20,Hill)  T(C20,Hill)  V(A20,Hill)  V(B20,Hill)  V(C20,Hill) 
All of the synthetic data sets consist of measurements over 16 consecutive time points. After the data sets were created, we added zeromean Gaussian noise (values are drawn from a normal random variable with a mean of zero and a variance of 0.15 times the maximum range of all the expression levels) [23].
In addition to the three artificial 5gene GRN systems, we used two real data sets obtained from 11 yeast cell cycle genes [11]. One data set (38 time points) was used for training, and the other (30 time points) for validation. The network structure of this 11gene yeast cell cycle system consists of 15 activating and 14 repressing influences [24].
To determine the role that the omega parameters play in GRN model inference, we reverseengineered a total of 192 GRN models from the 24 synthetic training data sets. Each of the 24 training data sets depicted in Table 1 was reverseengineered 4 times with the Hill (Eq. 1), and 4 times with the ANN (Eq. 2) rate law, with the following interval settings for the omega parameters: ω_{ ij } ∈ [−1, +1], ω_{ ij } ∈ [−5, +5], ω_{ ij } ∈ [−10, +10] and ω_{ ij } ∈ [−20, +20]. Notice, in the reverseengineered models, the parameters are free to assume any value within the given interval limits, whereas in the artificial systems (Section 3) the same parameters assume only the boundary values of these intervals (for synthesis activation and repression), and zero for no synthesis regulation (hence the first column in Table 1 does not show intervals but sets that contain exactly three elements).
In addition to the 192 GRN models we reverseengineered from the data generated from our artificial systems, we have reverseengineered 8 GRN models from the single training data set (Alpha 38) of the yeast cell cycle system using both the Hill and ANN rate laws with the same interval specifications for the omega parameters: ω_{ ij } ∈ [−1, +1], ω_{ ij } ∈ [−5, +5], ω_{ ij } ∈ [−10, +10] and ω_{ ij } ∈ [−20, +20].
Each of the 192 GRN models from synthetic data was validated against the corresponding independent validation data set, and the each of the 8 models inferred from the yeast cell cycle system was validated against the single independent validation data set (Alpha 30).
Results and discussion
Training errors of models of synthetic systems A, B, C which were created with ANN rate law.
System and Code  Training data from synthetic ANN SYSTEM  

ANN MODEL training error  Hill MODEL training error  
[1,+1]  [5,+5]  [10,+10]  [20,+20]  [1,+1]  [5,+5]  [10,+10]  [20,+20]  $\overline{x}$  s  
A  1, 0, +1  0.135  0.136  0.135  0.136  0.139  0.140  0.136  0.133  0.136  0.002 
B  0.161  0.160  0.160  0.160  0.176  0.163  0.159  0.162  0.163  0.006  
C  0.160  0.160  0.160  0.160  0.158  0.160  0.158  0.166  0.160  0.002  
A  5, 0, +5  0.137  0.136  0.139  0.137  0.142  0.141  0.136  0.143  0.139  0.003 
B  0.128  0.126  0.124  0.124  0.139  0.125  0.126  0.124  0.127  0.005  
C  0.152  0.152  0.152  0.152  0.153  0.155  0.151  0.153  0.152  0.001  
A  10,0,+10  0.156  0.156  0.156  0.156  0.186  0.160  0.158  0.148  0.160  0.011 
B  0.142  0.142  0.142  0.142  0.149  0.150  0.150  0.139  0.144  0.004  
C  0.144  0.142  0.142  0.142  0.147  0.150  0.148  0.149  0.146  0.003  
A  20,0,+20  0.132  0.136  0.136  0.134  0.141  0.137  0.138  0.131  0.136  0.003 
B  0.135  0.137  0.136  0.136  0.137  0.145  0.145  0.136  0.138  0.004  
C  0.131  0.130  0.130  0.130  0.132  0.130  0.130  0.131  0.130  0.001  
$\overline{x}$  0.143  0.143  0.143  0.143  0.150  0.146  0.145  0.143  ALL  ALL  
s  0.012  0.012  0.012  0.012  0.017  0.012  0.011  0.013  0.144  0.012 
Training errors of models of synthetic systems A, B, C which were created with Hill rate law.
System and Code  Training data from synthetic Hill SYSTEM  

ANN MODEL training error  Hill MODEL training error  
[1,+1]  [5,+5]  [10,+10]  [20,+20]  [1,+1]  [5,+5]  [10,+10]  [20,+20]  $\overline{x}$  s  
A  1, 0, +1  0.118  0.115  0.120  0.116  0.126  0.119  0.119  0.137  0.121  0.007 
B  0.140  0.143  0.143  0.143  0.147  0.141  0.141  0.138  0.142  0.003  
C  0.165  0.163  0.164  0.167  0.163  0.162  0.162  0.163  0.163  0.002  
A  5, 0, +5  0.252  0.254  0.252  0.252  0.134  0.133  0.134  0.133  0.193  0.064 
B  0.230  0.247  0.247  0.247  0.126  0.126  0.127  0.123  0.184  0.063  
C  0.348  0.348  0.348  0.348  0.132  0.132  0.131  0.133  0.240  0.116  
A  10,0,+10  0.470  0.471  0.470  0.470  0.153  0.152  0.152  0.153  0.311  0.170 
B  0.455  0.447  0.443  0.455  0.125  0.125  0.125  0.124  0.287  0.174  
C  0.432  0.432  0.432  0.432  0.126  0.112  0.111  0.112  0.274  0.170  
A  20,0,+20  0.586  0.586  0.586  0.586  0.194  0.175  0.168  0.168  0.381  0.219 
B  0.568  0.568  0.568  0.568  0.141  0.122  0.117  0.116  0.346  0.237  
C  0.492  0.492  0.492  0.492  0.194  0.140  0.124  0.123  0.318  0.187  
$\overline{x}$  0.355  0.355  0.355  0.356  0.147  0.136  0.134  0.135  ALL  ALL  
s  0.168  0.167  0.166  0.167  0.025  0.019  0.018  0.018  0.247  0.158 
Validation errors of models of synthetic systems A, B, C which were created with ANN rate law.
System and Code  Validation data from synthetic ANN SYSTEM  

ANN MODEL validation error  Hill MODEL validation error  
[1,+1]  [5,+5]  [10,+10]  [20,+20]  [1,+1]  [5,+5]  [10,+10]  [20,+20]  $\overline{x}$  s  
A  1, 0, +1  0.220  0.199  0.193  0.168  0.243  0.397  0.362  0.405  0.273  0.098 
B  0.168  0.140  0.166  0.164  0.261  0.166  0.266  0.431  0.220  0.097  
C  0.161  0.141  0.148  0.161  0.291  0.353  0.300  0.260  0.227  0.083  
A  5, 0, +5  0.228  0.217  0.216  0.199  0.304  0.382  0.342  0.463  0.294  0.096 
B  0.144  0.292  0.133  0.134  0.163  0.177  0.147  0.379  0.196  0.090  
C  0.160  0.150  0.141  0.140  0.244  0.254  0.209  0.312  0.201  0.064  
A  10,0,+10  0.163  0.159  0.155  0.198  0.329  0.298  0.255  0.375  0.242  0.086 
B  0.198  0.192  0.187  0.188  0.227  0.194  0.198  0.432  0.227  0.084  
C  0.144  0.167  0.159  0.181  0.236  0.277  0.265  0.369  0.225  0.077  
A  20,0,+20  0.183  0.209  0.216  0.213  0.313  0.355  0.404  0.426  0.290  0.097 
B  0.185  0.174  0.174  0.172  0.178  0.424  0.264  0.212  0.223  0.087  
C  0.243  0.186  0.233  0.183  0.303  0.262  0.245  0.418  0.259  0.075  
$\overline{x}$  0.183  0.186  0.177  0.175  0.258  0.295  0.271  0.374  ALL  ALL  
s  0.033  0.042  0.032  0.024  0.053  0.088  0.072  0.076  0.240  0.087 
Validation errors of models of synthetic systems A, B, C which were created with Hill rate law.
System and Code  Validation data from synthetic Hill SYSTEM  

ANN MODEL validation error  Hill MODEL validation error  
[1,+1]  [5,+5]  [10,+10]  [20,+20]  [1,+1]  [5,+5]  [10,+10]  [20,+20]  $\overline{x}$  s  
A  1, 0, +1  0.163  0.240  0.187  0.223  0.215  0.203  0.190  0.212  0.204  0.024 
B  0.173  0.173  0.174  0.171  0.189  0.185  0.195  0.184  0.180  0.009  
C  0.259  0.255  0.228  0.193  0.258  0.189  0.263  0.224  0.234  0.030  
A  5, 0, +5  0.398  0.403  0.403  0.403  0.201  0.209  0.203  0.198  0.302  0.106 
B  0.292  0.342  0.342  0.298  0.132  0.132  0.133  0.209  0.235  0.094  
C  0.386  0.434  0.337  0.435  0.148  0.145  0.148  0.144  0.272  0.138  
A  10,0,+10  0.483  0.481  0.507  0.482  0.163  0.164  0.169  0.171  0.327  0.172 
B  0.426  0.423  0.418  0.427  0.132  0.133  0.132  0.134  0.278  0.156  
C  0.516  0.556  0.488  0.556  0.165  0.153  0.153  0.152  0.343  0.201  
A  20,0,+20  0.553  0.553  0.552  0.552  0.185  0.167  0.160  0.161  0.361  0.206 
B  0.552  0.552  0.552  0.552  0.146  0.135  0.142  0.143  0.347  0.219  
C  0.535  0.563  0.563  0.533  0.194  0.138  0.131  0.134  0.349  0.215  
$\overline{x}$  0.395  0.415  0.396  0.402  0.177  0.163  0.168  0.172  ALL  ALL  
s  0.143  0.137  0.143  0.146  0.037  0.028  0.039  0.032  0.286  0.153 
Training and validation errors of cell cycle models.
System  Traing and validation data from Cell Cycle SYSTEM  

Cell Cycle (alpha 38)  Training error ANN MODEL  Training error Hill MODEL  
[1,+1]  [5,+5]  [10,+10]  [20,+20]  [1,+1]  [5,+5]  [10,+10]  [20,+20]  $\overline{x}$  s  
0.110  0.111  0.109  0.114  0.110  0.110  0.113  0.113  0.111  0.002  
Cell Cycle (alpha 30)  Validation error ANN MODEL  Validation error Hill MODEL  $\overline{x}$  s  
[1,+1]  [5,+5]  [10,+10]  [20,+20]  [1,+1]  [5,+5]  [10,+10]  [20,+20]  
0.220  0.416  0.725  0.213  0.201  0.195  0.214  0.214  0.300  0.187 
Training errors synthetic systems
First, we consider the training errors of the GRN models derived from the synthetic GRN systems in Tables 2 and 3. The training error's means and standard deviations are shown in the bottomright corner of the tables.

Training: S(ANN) → M (ANN): 0.1427 ± 0.0001.

Training: S(ANN) → M (Hill): 0.1459 ± 0.0023.

Training: S(Hill) → M (ANN): 0.3554 ± 0.0009.

Training: S(Hill) → M (Hill): 0.1381 ± 0.0035.
From the average mean training errors, we notice that both sets of Hill models have an average mean training error close to 0.14. This is comparable to average mean training error of the ANN model obtained from the ANN system's data. However, the mean training error (0.3554) of the ANN model obtained from the ANN system's data is more than twice that value. Since the ANN rate law (Eq. 2) has fewer parameters than the Hill rate law (Eq. 1), and hence a smaller degree of freedom, it is harder for the ANN models to fit data obtained from Hill systems. Hill models, on the other hand, can adapt easier to data generated from ANN systems.
While above observations are interesting, the most important information in the context of our investigation relates to the groups of 4 error values for a given system/model combination (e.g. 4 values highlighted in bold font in Table 2), as well as to entire columns of error values (e.g. blocks of errors in italic font in Table 3). Tables 2 and 3 highlight four horizontal groups of 4 training errors in bold; these groups have a standard deviation higher than 0.010. If anything, one would expect the errors to get smaller for larger omega intervals (from left to right), because larger omega intervals relate to a larger solution space. However, in most cases such a pattern is not observed. Indeed, even for the training errors in the bottom three rows in both tables (these were obtained from data of the three systems with large omega values: −20 and +20 for repression and activation, respectively), we cannot find a general improvement of training error for increasing omega intervals. For example, in Table 2 the two horizontal groups of 4 training errors highlighted in italic font do not show a clear pattern of decreasing training errors.
Furthermore, when we look at the profiles of the training errors in the columns of both tables, we notice a good pairwise similarity of training errors (at least within the four columns relating to the same system/model combination). This is illustrated by two columns highlighted in italic font in Table 3. This means that models inferred with different omega intervals show similar training errors for corresponding data sets. There does not seem to be an advantage of using larger omega intervals in the reverseengineering process.
Validation errors synthetic systems
The validation error of the inferred models characterizes the predictive power of the models. Tables 4 and 5 show the validation errors of the models inferred from the data of the synthetic systems depicted in Figure 3. The mean validation error of all 192 models inferred from the synthetic systems' data is 0.263 with a standard deviation of 0.127 (not shown in tables). So the mean validation error across all models is ca. 34% higher than the mean training error. The variation of the validation errors is similar to that of the training errors (Tables 2 and 3).

Validation: S(ANN) → M (ANN): 0.1801 ± 0.0076.

Validation: S(ANN) → M (Hill): 0.2994 ± 0.0146.

Validation: S(Hill) → M (ANN): 0.4019 ± 0.0039.

Validation: S(Hill) → M (Hill): 0.1701 ± 0.0050.
The average mean validation errors are consistent with the averages for the mean training errors, in that, the ANN models' predictive performance on the Hill system's data is much poorer than that of the other three models. In fact, the validation errors reveal that inferring models from data that was obtained from systems that were created with the same rate law (as the model), constitutes a considerable bias. The average mean errors for ANN models obtained from ANN system data, and for Hill models from Hill system data are quite low and similar. However, with mixed configurations (different rate law for system and model), we get much higher average mean validation errors. This relates to the important but frequently ignored issue of the modeling error. The modeling error is due to the fundamental imperfections that arise when we make abstractions of reality in the form of mathematical or computational models. A model, any model, is by definition an approximation of reality [25]. The modeling error quantifies how well the abstraction approximates reality. Conceptualizing a complex phenomena such as GRN systems as a mathematical or computational model is a relatively new modeling abstraction. More research is required to understand how to assess the modeling error in such approaches.
Looking at the data in Tables 4 and 5 in detail, we notice that things are less homogeneous than for training errors. This is to be expected, as predicting the timecourses for unseen stimuli is a much harder task than predicting the timecourses for known inputs. In Table 4 the groups for which the withingroup standard deviation is greater than 0.075 are highlighted in bold font. Surprisingly, there are many such groups in Hill/ANN model/system configurations. Still, in terms of the hypothesis we are testing, most groups of four do not show a pattern of decreasing validation error with increasing omega intervals. For example, the two horizontal groups of four validation errors highlighted in italic font in Table 5 illustrate two sets of validation errors that do not vary across the omega interval settings. Indeed, in some cases there is even an increase of error  and in other cases a slight decrease. Likewise, when we look at the vertical validation error profiles in columns (e.g. the two columns highlighted in italic font in Table 5), we notice a general pairwise similarity for each model group. These observations confirm our hypothesis that the absolute size of the interval for ω_{ ij } is not critical. Even when data is generated with large ω_{ ij } values, the reverseengineered models can approximate the data equally well with small and large ω_{ ij } ranges.
Training and validation errors yeast system

Training: S(CC) → M (ANN): 0.1110 ± 0.0019.

Training: S(CC) → M (Hill): 0.1116 ± 0.0018.

Validation: S(CC) → M (ANN): 0.3936 ± 0.2402.

Validation: S(CC) → M (Hill): 0.2058 ± 0.0094.
In terms of the mean training error, the two models perform almost identically. But the mean validation error of the ANN model is nearly twice that of the Hill model! This difference in predictive power is quite remarkable, even though we are testing only four omega conditions. We also observe that the variation (standard deviation) in the ANN model performance (validation error) is much higher than that of the Hill model. Clearly, the Hill rate law has more parameters and hence is more likely to fit complex curves. Still, that the ANN model mean validation error is nearly 100% higher than that of the Hill model (when the mean training errors are similar), seems to be an important observation.
We now analyze how the training and validation performance depends on the omega intervals. We observe essentially a similar pattern as in the evaluation of the synthetic systems. For the two groups of four training errors in Table 6 there seems to be hardly any variation in training error from smaller to larger omega intervals. In the four validation errors of the Hill model, we see a minor variation, but a slight rise in error as we move to larger omega intervals (if anything, the error should become smaller, as more solution possibilities are being explored). And in the validation errors of the ANN model, we notice a considerable variation in validation errors but no pattern of decrease in validation error from smaller to larger omega intervals. So overall, this seems to corroborate the results derived from the synthetic systems in Tables 2 and 3 (training errors), and Tables 4 and 5 (validation errors). It seems, that choosing large (and ad hoc) omega intervals does not make a real difference.
Conclusions
In this study, we focused on the automated reverseengineering (or inference) of generegulatory models from timecourse gene expression data. The "grand challenge" in this area is to infer dynamic (timeresolved) mechanistic (quantitative causeeffect) regulatory interactions from data [4]. Currently, this task is hampered by the lack of sufficient amounts of data in terms of stimulusresponse data sets from the same system. However, as experimental techniques improve and become more affordable, more and more relevant data is likely to be produced in the future. We anticipate that multistimulus data on the same system is likely to reveal more of the underlying mechanistic details of GRN systems, and modeling approaches as the one presented in this study will become a part of the standard toolbox [5].
The particular focus of this study was to investigate the role of the omega parameters within a particular class of semimechanistic mathematical GRN model formalisms or rate laws. In ANN and Hill laws [2, 13] and similar (e.g. the synergisticsystem [16]) rate laws, the ω_{ ij } parameters simultaneously represent the presence or absence of transcript synthesis regulators (a discrete concept) and the strength of their regulatory influence (a continuous concept). When we reverseengineer GRN models from timeseries gene expression data, we need to define reasonable limits for these parameters, to avoid an excessively large solution search space. Often, the choice of the size of the ω_{ ij } intervals is defined in an ad hoc way or determined by trialanderror experimentation. The hypothesis we tested in this study was that limiting ω_{ ij } to ω_{ ij } ∈ [−1, +1] facilitates full expression without loss in accuracy of the inferred models.
To test this hypothesis, we created various data sets from three synthetic 5gene systems (A, B, and C; see Figure 3) based on the ANN and Hill rate laws defined by Eqs. 1 and 2, and used two publicly available data sets from an 11gene cell cycle system [11, 24]. From the synthetic systems, we generated 192 training and 192 validation data sets under different omega interval conditions. We explored how the model training errors and model validation errors (predictive power) vary in relation to different settings of the omega interval. Our results suggest that the absolute size of the omega interval does not seem to have any effect on the models' predictive performance (validation error).
This result has important consequences for reverseengineering algorithms that estimate concrete values of ω_{ ij } and other model parameters. In particular, it is not necessary to choose an excessively large interval range for ω_{ ij }. Because we need to specify a ω_{ ij } interval for all possible n^{2} regulators of a GRN, large ω_{ ij } intervals have a considerable impact on the computational complexity (size of parameter solution space) of the model inference algorithm. Knowing that ω_{ ij } ∈ [−1, +1] is sufficient is likely to improve the computing performance of such algorithms.
Clearly, more research is needed to form a more comprehensive view on the merits and limitations of GRN model inference. In particular, we need methods and tools that are capable of inferring reliable and interpretable mechanistic generegulatory networks from data. While empirical studies like the one presented here are important, more theoretical investigations are needed to establish how much information relating to the mechanistic generegulatory network structure of the underlying GRN system is actually contained in the experimental data. We need also more studies like that of Cantone and colleagues [8] that provide the basis for comprehensive studies based on real data.
Declarations
Acknowledgements
This research received funding from the MAPPER EUFP7 project (grant no. RI261507) and was supported in part by PLGrid infrastructure.
Declaration
This project/study was funded through the MAPPER EUFP7 project (grant no. RI261507). Funding for this article has come from the Biomedical Sciences Research Institute, University of Ulster, Coleraine, United Kingdom.
This article has been published as part of BMC Systems Biology Volume 9 Supplement 5, 2015: Selected articles from the IEE International Conference on Bioinformatics and Biomedicine (BIBM 2014): Bioinformatics. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcsystbiol/supplements/9/S5.
Authors’ Affiliations
References
 Baker S, Kramer B: Systems biology and cancer: promises and perils. Prog Biophys Mol Biol. 2011, 106 (2): 410413. 10.1016/j.pbiomolbio.2011.03.002.PubMed CentralView ArticlePubMedGoogle Scholar
 Alon U: . An Introduction to systems biology: Design principles of biological circuits. 2006, CRC Press, Taylor & Francis Group, LondonGoogle Scholar
 Karlebach G, Shamir R: Modelling and analysis of gene regulatory networks. Nat Rev Mol Cell Biol. 2008, 9 (10): 770780. 10.1038/nrm2503.View ArticlePubMedGoogle Scholar
 Marbach D, Costello J, Küffner R, Vega N, Prill R, Camacho D, et al: Wisdom of crowds for robust gene network inference. Nature Methods. 2012, 9 (8): 796804. 10.1038/nmeth.2016.PubMed CentralView ArticlePubMedGoogle Scholar
 Kennedy N, Mizeranschi A, Thompson P, Zheng H, Dubitzky W: Reverseengineering of gene regulation models from multicondition experiments. IEEE Symposium Series on Computational Intelligence 2013 (SSCI 2013). 2013, 112119.Google Scholar
 Villaverde AF, Banga JR: Reverse engineering and identification in systems biology: strategies, perspectives and challenges. Journal of the Royal Society Interface. 2014, 11 (91): 20130505PubMed CentralView ArticleGoogle Scholar
 Swain MT, Mandel JJ, Dubitzky W: Comparative study of three commonly used continuous deterministic methods for modeling gene regulation networks. BMC Bioinformatics. 2010, 11: 45910.1186/1471210511459.PubMed CentralView ArticlePubMedGoogle Scholar
 Cantone I, Marucci L, Iorio F, Ricci M, Belcastro V, Bansal M, et al: A yeast synthetic network for in vivo assessment of reverseengineering and modeling approaches. Cell. 2009, 137 (1): 172181. 10.1016/j.cell.2009.01.055.View ArticlePubMedGoogle Scholar
 Bongard J, Lipson H: Automated reverse engineering of nonlinear dynamical systems. Proc Natl Acad Sci U S A. 2007, 104 (24): 99439948. 10.1073/pnas.0609476104.PubMed CentralView ArticlePubMedGoogle Scholar
 Barlas Y: Model validation in systems dynamics. International Systems Dynamics Conference. 1994, 110.Google Scholar
 Pramila T, Wu W, Miles S, Noble WS, Breeden LL: The Forkhead transcription factor Hcm1 regulates chromosome segregation genes and fills the Sphase gap in the transcriptional circuitry of the cell cycle. Genes and Development. 2006, 20 (16): 22662278. 10.1101/gad.1450606.PubMed CentralView ArticlePubMedGoogle Scholar
 Hill AV: The heat produced in contracture and muscular tone. Journal of Physiology. 1910, 40 (5): 389403. 10.1113/jphysiol.1910.sp001377.PubMed CentralView ArticlePubMedGoogle Scholar
 Vohradský J: Neural network model of gene expression. The FASEB Journal. 2001, 15 (3): 846854. 10.1096/fj.000361com.View ArticlePubMedGoogle Scholar
 Setty Y, Mayo A, Surette M, Alon U: Detailed map of a cisregulatory input function. Proc Natl Acad Sci U S . 2003, 100 (13): 77027707. 10.1073/pnas.1230759100.View ArticleGoogle Scholar
 McCulloch WS, Pitts W: A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics. 1943, 5 (4): 115133. 10.1007/BF02478259.View ArticleGoogle Scholar
 Savageau MA: . Mathematical and Computer Modelling. 1988, 11: 546551.View ArticleGoogle Scholar
 Ben Belgacem M, Chopard B, Borgdorff J, Mamonski M, Rycerz K, Harezlak D: Distributed Multiscale Computations Using the MAPPER Framework. Procedia Computer Science. 2013, 18 (0): 11061115.View ArticleGoogle Scholar
 Borgdorff J, Falcone JL, Lorenz E, BonaCasas C, Chopard B, Hoekstra AG: . Journal of Parallel and Distributed Computing. 2013, 73 (4): 465483. 10.1016/j.jpdc.2012.12.011.View ArticleGoogle Scholar
 Groen D, Borgdorff J, BonaCasas C, Hetherington J, Nash RW, Zasada SJ, et al: Flexible composition and execution of high performance, high fidelity multiscale biomedical simulations. Interface Focus. 2013, 3 (2): 2012008710.1098/rsfs.2012.0087.PubMed CentralView ArticlePubMedGoogle Scholar
 Kennedy J, Eberhart R: Particle swarm optimization. Neural Networks, 1995. Proceedings., IEEE International Conference on. 1995, 4: 19421948.Google Scholar
 Pedersen MEH: Good parameters for differential evolution. Hvass Laboratories Technical Report HL1002. 2010Google Scholar
 Hlavacek WS, Savageau MA: . J Mol Biol. 1996, 255 (1): 121139. 10.1006/jmbi.1996.0011.View ArticlePubMedGoogle Scholar
 Nykter M, Aho T, Ahdesmäki M, Ruusuvuori P, Lehmussola A, YliHarja O: Simulation of microarray data with realistic characteristics. BMC Bioinformatics. 2006, 7 (349):Google Scholar
 Li F, Long T, Lu Y, Ouyang Q, Tang C: The yeast cellcycle network is robustly designed. Proc Natl Acad Sci U S A. 2004, 101 (14): 47814786. 10.1073/pnas.0305937101.PubMed CentralView ArticlePubMedGoogle Scholar
 Horstemeyer M: . Practical aspects of computational chemistry. Edited by: J. Leszczynski, M. Shukla. 2009, SpringerVerlag, 87135.View ArticleGoogle Scholar
Copyright
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.