Quantification of degeneracy in Hodgkin–Huxley neurons on Newman–Watts small world network

Man, Menghua; Zhang, Ya; Ma, Guilei; Friston, Karl J.; Liu, Shanghe

doi:10.1016/j.jtbi.2016.05.004

Cited by 9 publications

(17 citation statements)

References 62 publications

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“…Although a variety of types of biological networks are thought to exhibit degeneracy, previous theoretical work has primarily focused on networks with weighted connections [33,40,59]. In this study, we demonstrate that degeneracy measures are also suitable for RBNs, frequently used to model gene regulation mechanisms.…”

Section: Discussionmentioning

confidence: 70%

“…This area shows a characteristic shape of the degeneracy function: a non-zero value that declines to zero as perturbed subset size k approaches k = O , following an increase that is “higher than would be expected from a linear increase” [33]. This characteristic shape has furthermore been replicated in networks composed of in Hodgkin-Huxley neurons [40]. In our study, the analogous condition for such illustration of degeneracy is where no edges are lesioned in the networks (Fig2a).…”

Section: Resultsmentioning

confidence: 97%

“…Degeneracy (or alternatively, 'distributed redundancy' [30], 'distributed robustness' [31], 'functional redundancy' [25], 'extrinsic buffering' [32]) describes the ability of components in a biological system that are structurally different to carry out the same or similar functions [20,21,21,28,[33][34][35][36][37][38][39][40]. As difference in the structure implies different functions [41], under certain conditions degenerate .…”

Section: Introductionmentioning

confidence: 99%

“…Degeneracy (or alternatively, ‘distributed redundancy’ [30], ‘distributed robustness’ [31], ‘functional redundancy’ [25], ‘extrinsic buffering’ [32]) describes the ability of components in a biological system that are structurally different to carry out the same or similar functions [20,21,21,28,33–40]. As difference in the structure implies different functions [41], under certain conditions degenerate components do not necessarily show functional variety [42,43] but instead, each degenerate component is responsible for a (set of) function(s) which is initially determined by their biochemical(/physical) structure [9,21,31].…”

Section: Introductionmentioning

confidence: 99%

“…If the output activity of the system is not affected by the change (e.g., perturbation) in a subset’s state, then the system is highly degenerate with regard to the function that is performed by that subset. This information theoretic measure of degeneracy is, first, applied to highly abstract networks in the work by Tononi et al [33] which is followed by applications to the weighted networks with a high degree of biological fidelity (e.g., Hodgkin-Huxley type neural networks [40] and genetic networks with epistasis [59]).…”

Section: Introductionmentioning

confidence: 99%

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Degeneracy measures in biologically plausible random Boolean networks

Kocaoglu

Alexander

2021

Preprint

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Biological systems show diversity in terms of the underlying network structure and the governing rules of such networks. Yet, different types of biological networks may develop similar adaptation strategies in face of environmental changes. Degeneracy refers to the ability to compensate for compromised function without the need for a redundant component in the system. Highly degenerate systems show resilience to perturbations and damage because the system can compensate for compromised function due to reconfiguration of the underlying network dynamics.Although formal definitions of degeneracy have been proposed, these definitions have only been tested in relatively simple networks involving weighted connections between network nodes. In this study, we test an information theoretic definition of degeneracy on random Boolean networks, frequently used to model gene regulatory networks. Random Boolean networks are discrete dynamical systems with binary connectivity and thus, these networks are well-suited for tracing information flow and the causal effects. By generating networks with random binary wiring diagrams, we test the effects of systematic lesioning of connections and perturbations of the network nodes on the degeneracy measure.Our analysis shows that degeneracy, on average, is the highest in networks in which ~20% of the connections are lesioned while 50% of the nodes are perturbed. Moreover, our results for the networks with no lesions and the fully-lesioned networks are comparable to the degeneracy measures from weighted networks, thus we show that the degeneracy measure is applicable to different networks. Such a generalized applicability implies that degeneracy can be used to make predictions about the variety of systems’ ability to recover function.Author SummaryDegeneracy – the ability of structurally different elements to perform similar functions – is a property of many biological systems. Systems exhibiting a high degree of degeneracy continue to exhibit the same macroscopic behavior following a lesion even though the underlying network dynamics are significantly different. Degeneracy thus suggests how biological systems can thrive despite changes to internal and external demands. Although degeneracy is a feature of network topologies and seems to be implicated in a wide variety of biological processes, research on degeneracy in biological networks is mostly limited to weighted networks (e.g., neural networks). To date, there has been no extensive investigation of information theoretic measures of degeneracy in other types of biological networks. In this paper, we apply existing approaches for quantifying degeneracy to random Boolean networks used for modeling biological gene regulatory networks. Using random Boolean networks with randomly generated rulesets to generate synthetic gene expression data sets, we systematically investigate the effect of network lesions on measures of degeneracy. Our results are comparable to measures of degeneracy using weighted networks, and this suggests that degeneracy measures may be a useful tool for investigating gene regulatory networks.

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Section: Discussionmentioning

confidence: 70%

Section: Resultsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Degeneracy measures in biologically plausible random Boolean networks

Kocaoglu

Alexander

2021

Preprint

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Degeneracy in the nervous system: from neuronal excitability to neural coding

Kamaleddin

2021

BioEssays

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Degeneracy is ubiquitous across biological systems where structurally different elements can yield a similar outcome. Degeneracy is of particular interest in neuroscience too. On the one hand, degeneracy confers robustness to the nervous system and facilitates evolvability: Different elements provide a backup plan for the system in response to any perturbation or disturbance. On the other, a difficulty in the treatment of some neurological disorders such as chronic pain is explained in light of different elements all of which contribute to the pathological behavior of the system. Under these circumstances, targeting a specific element is ineffective because other elements can compensate for this modulation. Understanding degeneracy in the physiological context explains its beneficial role in the robustness of neural circuits. Likewise, understanding degeneracy in the pathological context opens new avenues of discovery to find more effective therapies.

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Degeneracy in epilepsy: multiple routes to hyperexcitable brain circuits and their repair

et al. 2023

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Due to its complex and multifaceted nature, developing effective treatments for epilepsy is still a major challenge. To deal with this complexity we introduce the concept of degeneracy to the field of epilepsy research: the ability of disparate elements to cause an analogous function or malfunction. Here, we review examples of epilepsy-related degeneracy at multiple levels of brain organisation, ranging from the cellular to the network and systems level. Based on these insights, we outline new multiscale and population modelling approaches to disentangle the complex web of interactions underlying epilepsy and to design personalised multitarget therapies.

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Quantification of degeneracy in Hodgkin–Huxley neurons on Newman–Watts small world network

Cited by 9 publications

References 62 publications

Degeneracy measures in biologically plausible random Boolean networks

Degeneracy measures in biologically plausible random Boolean networks

Degeneracy in the nervous system: from neuronal excitability to neural coding

Degeneracy in epilepsy: multiple routes to hyperexcitable brain circuits and their repair

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