Statistical method for estimation of the predictive power of a gene circuit model

2014 ◽  
Vol 12 (02) ◽  
pp. 1441002 ◽  
Author(s):  
Ekaterina Myasnikova ◽  
Konstantin N. Kozlov
Keyword(s):  
Predictive Power ◽  
Expression Patterns ◽  
Specific Aspect ◽  
Circuit Model ◽  
Correct Prediction ◽  
Model Parameters ◽  
Gene Circuit ◽  
Gap Gene ◽  
High Predictive Power ◽  
Low Sensitivity

In this paper, a specific aspect of the prediction problem is considered: high predictive power is understood as a possibility to reproduce correct behavior of model solutions at predefined values of a subset of parameters. The problem is discussed in the context of a specific mathematical model, the gene circuit model for segmentation gap gene system in early Drosophila development. A shortcoming of the model is that it cannot be used for predicting the system behavior in mutants when fitted to wild type (WT) data. In order to answer a question whether experimental data contain enough information for the correct prediction we introduce two measures of predictive power. The first measure reveals the biologically substantiated low sensitivity of the model to parameters that are responsible for correct reconstruction of expression patterns in mutants, while the second one takes into account their correlation with the other parameters. It is demonstrated that the model solution, obtained by fitting to gene expression data in WT and Kr - mutants simultaneously, and exhibiting the high predictive power, is characterized by much higher values of both measures than those fitted to WT data alone. This result leads us to the conclusion that information contained in WT data is insufficient to reliably estimate the large number of model parameters and provide predictions of mutants.

scholarly journals Classification-based Inference of Dynamical Models of Gene Regulatory Networks

2019 ◽  
Author(s):  
David A. Fehr ◽  
Manu ◽  
Yen Lee Loh
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Linear Optimization ◽  
Model Parameters ◽  
Gene Circuit ◽  
Gap Gene ◽  
Non Linear ◽  
Gene Regulatory

AbstractCell-fate decisions during development are controlled by densely interconnected gene regulatory networks (GRNs) consisting of many genes. Inferring and predictively modeling these GRNs is crucial for understanding development and other physiological processes. Gene circuits, coupled differential equations that represent gene product synthesis with a switch-like function, provide a biologically realistic framework for modeling the time evolution of gene expression. However, their use has been limited to smaller networks due to the computational expense of inferring model parameters from gene expression data using global non-linear optimization. Here we show that the switch-like nature of gene regulation can be exploited to break the gene circuit inference problem into two simpler optimization problems that are amenable to computationally efficient supervised learning techniques. We present FIGR (Fast Inference of Gene Regulation), a novel classification-based inference approach to determining gene circuit parameters. We demonstrate FIGR’s effectiveness on synthetic data as well as experimental data from the gap gene system of Drosophila. FIGR is faster than global non-linear optimization by nearly three orders of magnitude and its computational complexity scales much better with GRN size. On a practical level, FIGR can accurately infer the biologically realistic gap gene network in under a minute on desktop-class hardware instead of requiring hours of parallel computing. We anticipate that FIGR would enable the inference of much larger biologically realistic GRNs than was possible before. FIGR Source code is freely available at http://github.com/mlekkha/FIGR.

Explanation Plus Prediction—The Logical Focus of Project Management Research

2021 ◽  
pp. 875697282199994
Author(s):  
Joseph F. Hair ◽  
Marko Sarstedt
Keyword(s):  
Project Management ◽  
Statistical Models ◽  
Predictive Power ◽  
Mean Absolute Error ◽  
Explanatory Power ◽  
Absolute Error ◽  
Model Parameters ◽  
Mean Square ◽  
Management Research ◽  
The Mean

Most project management research focuses almost exclusively on explanatory analyses. Evaluation of the explanatory power of statistical models is generally based on F-type statistics and the R 2 metric, followed by an assessment of the model parameters (e.g., beta coefficients) in terms of their significance, size, and direction. However, these measures are not indicative of a model’s predictive power, which is central for deriving managerial recommendations. We recommend that project management researchers routinely use additional metrics, such as the mean absolute error or the root mean square error, to accurately quantify their statistical models’ predictive power.

Emx1 and Emx2 functions in development of dorsal telencephalon

Development ◽  
1997 ◽  
Vol 124 (1) ◽  
pp. 101-111 ◽  
Author(s):  
M. Yoshida ◽  
Y. Suda ◽  
I. Matsuo ◽  
N. Miyamoto ◽  
N. Takeda ◽  
...  
Keyword(s):  
Dentate Gyrus ◽  
Expression Patterns ◽  
Lateral Shift ◽  
Cerebral Hemispheres ◽  
Limbic Cortex ◽  
Lateral Region ◽  
Gap Gene ◽  
Positive Region ◽  
Dorsal Telencephalon ◽  
And Behavior

The genes Emx1 and Emx2 are mouse cognates of a Drosophila head gap gene, empty spiracles, and their expression patterns have suggested their involvement in regional patterning of the forebrain. To define their functions we introduced mutations into these loci. The newborn Emx2 mutants displayed defects in archipallium structures that are believed to play essential roles in learning, memory and behavior: the dentate gyrus was missing, and the hippocampus and medial limbic cortex were greatly reduced in size. In contrast, defects were subtle in adult Emx1 mutant brain. In the early developing Emx2 mutant forebrain, the evagination of cerebral hemispheres was reduced and the roof between the hemispheres was expanded, suggesting the lateral shift of its boundary. Defects were not apparent, however, in the region where Emx1 expression overlaps that of Emx2, nor was any defect found in the early embryonic forebrain caused by mutation of the Emx1 gene, of which expression principally occurs within the Emx2-positive region. Emx2 most likely delineates the palliochoroidal boundary in the absence of Emx1 expression during early dorsal forebrain patterning. In the more lateral region of telencephalon, Emx2-deficiency may be compensated for by Emx1 and vice versa. Phenotypes of newborn brains also suggest that these genes function in neurogenesis corresponding to their later expressions.

Dose-dependent regulation of pair-rule stripes by gap proteins and the initiation of segment polarity

Development ◽  
1990 ◽  
Vol 110 (3) ◽  
pp. 759-767 ◽  
Author(s):  
R. Warrior ◽  
M. Levine
Keyword(s):  
Expression Patterns ◽  
Gene Products ◽  
Embryonic Cells ◽  
Periodic Patterns ◽  
Threshold Response ◽  
Gap Gene ◽  
Relative Placement ◽  
Segment Polarity ◽  
Pair Rule Gene ◽  
Pair Rule

A key step in Drosophila segmentation is the establishment of periodic patterns of pair-rule gene expression in response to gap gene products. From an examination of the distribution of gap and pair-rule proteins in various mutants, we conclude that the on/off periodicity of pair-rule stripes depends on both the exact concentrations and combinations of gap proteins expressed in different embryonic cells. It has been suggested that the distribution of gap gene products depends on cross-regulatory interactions among these genes. Here we provide evidence that autoregulation also plays an important role in this process since there is a reduction in the levels of Kruppel (Kr) RNA and protein in a Kr null mutant. Once initiated by the gap genes each pair-rule stripe is bell shaped and has ill-defined margins. By the end of the fourteenth nuclear division cycle, the stripes of the pair-rule gene even-skipped (eve) sharpen and polarize, a process that is essential for the precisely localized expression of segment polarity genes. This sharpening process appears to depend on a threshold response of the eve promoter to the combinatorial action of eve and a second pair-rule gene hairy. The eve and hairy expression patterns overlap but are out of register and the cells of maximal overlap form the anterior margin of the polarized eve stripe. We propose that the relative placement of the eve and hairy stripes may be an important factor in the initiation of segment polarity.

scholarly journals Inversion of a large-scale circuit model reveals a cortical hierarchy in the dynamic resting human brain

2019 ◽  
Vol 5 (1) ◽  
pp. eaat7854 ◽  
Author(s):  
Peng Wang ◽  
Ru Kong ◽  
Xiaolu Kong ◽  
Raphaël Liégeois ◽  
Csaba Orban ◽  
...  
Keyword(s):  
Large Scale ◽  
Meta Analysis ◽  
Circuit Model ◽  
Model Parameters ◽  
Optimization Approach ◽  
Functional Magnetic Resonance ◽  
Cortical Hierarchy ◽  
Out Of Sample ◽  
External Stimuli ◽  
Recurrent Connections

We considered a large-scale dynamical circuit model of human cerebral cortex with region-specific microscale properties. The model was inverted using a stochastic optimization approach, yielding markedly better fit to new, out-of-sample resting functional magnetic resonance imaging (fMRI) data. Without assuming the existence of a hierarchy, the estimated model parameters revealed a large-scale cortical gradient. At one end, sensorimotor regions had strong recurrent connections and excitatory subcortical inputs, consistent with localized processing of external stimuli. At the opposing end, default network regions had weak recurrent connections and excitatory subcortical inputs, consistent with their role in internal thought. Furthermore, recurrent connection strength and subcortical inputs provided complementary information for differentiating the levels of the hierarchy, with only the former showing strong associations with other macroscale and microscale proxies of cortical hierarchies (meta-analysis of cognitive functions, principal resting fMRI gradient, myelin, and laminar-specific neuronal density). Overall, this study provides microscale insights into a macroscale cortical hierarchy in the dynamic resting brain.

scholarly journals Predicting Liver Disease Risk Using a Combination of Common Clinical Markers: A Screening Model from Routine Health Check-Up

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yi Wang ◽  
Yanni Li ◽  
Xiaoyi Wang ◽  
Ranko Gacesa ◽  
Jie Zhang ◽  
...  
Keyword(s):  
Liver Disease ◽  
Early Detection ◽  
Predictive Power ◽  
Disease Screening ◽  
Health Check ◽  
Initial Presentation ◽  
Screening Tools ◽  
Clinical Markers ◽  
Liver Disorders ◽  
High Predictive Power

Background. Early detection is crucial for the prognosis of patients with autoimmune liver disease (AILD). Due to the relatively low incidence, developing screening tools for AILD remain a challenge. Aims. To analyze clinical characteristics of AILD patients at initial presentation and identify clinical markers, which could be useful for disease screening and early detection. Methods. We performed observational retrospective study and analyzed 581 AILD patients who were hospitalized in the gastroenterology department and 1000 healthy controls who were collected from health management center. Baseline characteristics at initial presentation were used to build regression models. The model was validated on an independent cohort of 56 patients with AILD and 100 patients with other liver disorders. Results. Asymptomatic AILD individuals identified by the health check-up are increased yearly (from 31.6% to 68.0%, p<0.001). The cirrhotic rates at an initial presentation are decreased in the past 18 years (from 52.6% to 20.0%, p<0.001). Eight indicators, which are common in the health check-up, are independent risk factors of AILD. Among them, abdominal lymph node enlargement (LN) positive is the most significant different (OR 8.85, 95% CI 2.73-28.69, p<0.001). The combination of these indicators shows high predictive power (AUC=0.98, sensitivity 89.0% and specificity 96.4%) for disease screening. Except two liver or cholangetic injury makers, the combination of AGE, GENDER, GLB, LN, concomitant extrahepatic autoimmune diseases, and familial history also shows a high predictive power for AILD in other liver disorders (AUC=0.91). Conclusion. Screening for AILD with described parameters can detect AILD in routine health check-up early, effectively and economically. Eight variables in routine health check-up are associated with AILD and the combination of them shows good ability of identifying high-risk individuals.

scholarly journals Parameter estimation for functional–structural plant models when data are scarce: using multiple patterns for rejecting unsuitable parameter sets

2020 ◽  
Vol 126 (4) ◽  
pp. 559-570 ◽  
Author(s):  
Ming Wang ◽  
Neil White ◽  
Jim Hanan ◽  
Di He ◽  
Enli Wang ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Predictive Power ◽  
Model Performance ◽  
Persea Americana ◽  
Strong Interactions ◽  
Model Parameters ◽  
Timely Manner ◽  
Complex Interactions ◽  
Output Uncertainty ◽  
Parameter Values

Abstract Background and Aims Functional–structural plant (FSP) models provide insights into the complex interactions between plant architecture and underlying developmental mechanisms. However, parameter estimation of FSP models remains challenging. We therefore used pattern-oriented modelling (POM) to test whether parameterization of FSP models can be made more efficient, systematic and powerful. With POM, a set of weak patterns is used to determine uncertain parameter values, instead of measuring them in experiments or observations, which often is infeasible. Methods We used an existing FSP model of avocado (Persea americana ‘Hass’) and tested whether POM parameterization would converge to an existing manual parameterization. The model was run for 10 000 parameter sets and model outputs were compared with verification patterns. Each verification pattern served as a filter for rejecting unrealistic parameter sets. The model was then validated by running it with the surviving parameter sets that passed all filters and then comparing their pooled model outputs with additional validation patterns that were not used for parameterization. Key Results POM calibration led to 22 surviving parameter sets. Within these sets, most individual parameters varied over a large range. One of the resulting sets was similar to the manually parameterized set. Using the entire suite of surviving parameter sets, the model successfully predicted all validation patterns. However, two of the surviving parameter sets could not make the model predict all validation patterns. Conclusions Our findings suggest strong interactions among model parameters and their corresponding processes, respectively. Using all surviving parameter sets takes these interactions into account fully, thereby improving model performance regarding validation and model output uncertainty. We conclude that POM calibration allows FSP models to be developed in a timely manner without having to rely on field or laboratory experiments, or on cumbersome manual parameterization. POM also increases the predictive power of FSP models.

Sign in / Sign up

Export Citation Format

Share Document