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Maximal probability reconstruction vs. gradient descent reconstruction. Maximal probability reconstruction vs. gradient descent reconstruction of trajectories of the Boolean XOR (left) and Boolean NOR (right) networks. The gradient descent algorithm is given an inaccurate structure. The rows correspond to the time points and columns to the network nodes. For display purposes, only a prefix of the trajectory is shown. The yellow color represents mistakes, i.e., values different than the real Boolean values, and orange represents a correct value. In each of the two comparisons, the maximal probability reconstruction is presented to the left of the gradient descent reconstruction. Overall, the gradient descent is more accurate than the maximal probability reconstruction despite the imperfect structures that are given to it as input. The XOR network's trajectory reconstruction is not affected by the error in structure, while the NOR network's reconstruction is slightly less accurate. The percentages of incorrect reconstructed values for maximal probability reconstruction are 17.6% (XOR) and 18% (NOR), and for the gradient descent reconstruction, 6.7% (XOR) and 3% (NOR).
Source publication
Networks of molecular interactions regulate key processes in living cells. Therefore, understanding their functionality is a high priority in advancing biological knowledge. Boolean networks are often used to describe cellular networks mathematically and are fitted to experimental datasets. The fitting often results in ambiguities since the interpr...
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Citations
... There are, however, few well-identified trends in various fields that can be used to form a composite approach, i.e., a combination of different modelling tools. Those trends and approaches include rumour propagation [1], network propagation in gene modelling [27], [36], [75], and the study of the impact of social media during pandemics [14]. In [49], stress is considered to be a 3-factor composite model: 1) an event stress factor (high volume of work), 2) a time pressure factor (limited time resource needed to complete the pending tasks), and 3) an effective fatigue factor (accumulated tiredness). ...
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... The fundamental tenet of computational genomics is the network propagation using the discovered data [27], [39], [36], [75]. This is a system identification problem aiming at building the mathematical models of dynamical systems from measured data. ...
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Background
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Results
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Conclusions
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Background
Intracellular signaling networks transmit signals from the cell membrane to the nucleus, via biochemical interactions. The goal is to regulate some target molecules, to properly control the cell function. Regulation of the target molecules occurs through the communication of several intermediate molecules that convey specific signals originated from the cell membrane to the specific target outputs.ResultsIn this study we propose to model intracellular signaling network as communication channels. We define the fundamental concepts of transmission error and signaling capacity for intracellular signaling networks, and devise proper methods for computing these parameters. The developed systematic methodology quantitatively shows how the signals that ligands provide upon binding can be lost in a pathological signaling network, due to the presence of some dysfunctional molecules. We show the lost signals result in message transmission error, i.e., incorrect regulation of target proteins at the network output. Furthermore, we show how dysfunctional molecules affect the signaling capacity of signaling networks and how the contributions of signaling molecules to the signaling capacity and signaling errors can be computed. The proposed approach can quantify the role of dysfunctional signaling molecules in the development of the pathology. We present experimental data on caspese3 and T cell signaling networks to demonstrate the biological relevance of the developed method and its predictions.Conclusions
This study demonstrates how signal transmission and distortion in pathological signaling networks can be modeled and studied using the proposed methodology. The new methodology determines how much the functionality of molecules in a network can affect the signal transmission and regulation of the end molecules such as transcription factors. This can lead to the identification of novel critical molecules in signal transduction networks. Dysfunction of these critical molecules is likely to be associated with some complex human disorders. Such critical molecules have the potential to serve as proper targets for drug discovery.
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