scholarly journals How causal analysis can reveal autonomy in models of biological systems

2017 ◽  
Vol 375 (2109) ◽  
pp. 20160358 ◽  
Author(s):  
William Marshall ◽  
Hyunju Kim ◽  
Sara I. Walker ◽  
Giulio Tononi ◽  
Larissa Albantakis
Keyword(s):  
Cell Cycle ◽  
Biological Systems ◽  
Causal Analysis ◽  
Local Maxima ◽  
Boolean Network Model ◽  
Real Cell ◽  
Individual System ◽  
Integrated Or ◽  
Model Features ◽  
Integrated Information Theory

Standard techniques for studying biological systems largely focus on their dynamical or, more recently, their informational properties, usually taking either a reductionist or holistic perspective. Yet, studying only individual system elements or the dynamics of the system as a whole disregards the organizational structure of the system—whether there are subsets of elements with joint causes or effects, and whether the system is strongly integrated or composed of several loosely interacting components. Integrated information theory offers a theoretical framework to (1) investigate the compositional cause–effect structure of a system and to (2) identify causal borders of highly integrated elements comprising local maxima of intrinsic cause–effect power. Here we apply this comprehensive causal analysis to a Boolean network model of the fission yeast ( Schizosaccharomyces pombe ) cell cycle. We demonstrate that this biological model features a non-trivial causal architecture, whose discovery may provide insights about the real cell cycle that could not be gained from holistic or reductionist approaches. We also show how some specific properties of this underlying causal architecture relate to the biological notion of autonomy. Ultimately, we suggest that analysing the causal organization of a system, including key features like intrinsic control and stable causal borders, should prove relevant for distinguishing life from non-life, and thus could also illuminate the origin of life problem. This article is part of the themed issue ‘Reconceptualizing the origins of life’.

scholarly journals MicroRNA-Mediated Regulation in Biological Systems with Oscillatory Behavior

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Zhiyong Zhang ◽  
Fengdan Xu ◽  
Zengrong Liu ◽  
Ruiqi Wang ◽  
Tieqiao Wen
Keyword(s):  
Cell Cycle ◽  
Biological Systems ◽  
Noncoding Rnas ◽  
Oscillatory Behavior ◽  
Fine Tuning ◽  
Base Pairing ◽  
Periodic Behavior ◽  
Small Noncoding Rnas ◽  
Regulatory Circuits ◽  
Mrna Transcripts

As a class of small noncoding RNAs, microRNAs (miRNAs) regulate stability or translation of mRNA transcripts. Some reports bring new insights into possible roles of microRNAs in modulating cell cycle. In this paper, we focus on the mechanism and effectiveness of microRNA-mediated regulation in the cell cycle. We first describe two specific regulatory circuits that incorporate base-pairing microRNAs and show their fine-tuning roles in the modulation of periodic behavior. Furthermore, we analyze the effects ofmiR369-3on the modulation of the cell cycle, confirming thatmiR369-3plays a role in shortening the period of the cell cycle. These results are consistent with experimental observations.

A hybrid model of cell cycle in mammals

2016 ◽  
Vol 14 (01) ◽  
pp. 1640001 ◽  
Author(s):  
Jonathan Behaegel ◽  
Jean-Paul Comet ◽  
Gilles Bernot ◽  
Emilien Cornillon ◽  
Franck Delaunay
Keyword(s):  
Cell Cycle ◽  
Hybrid Model ◽  
Biological Systems ◽  
Modeling Framework ◽  
Variable Model ◽  
The Core ◽  
Mammalian Cell Cycle ◽  
Hybrid Framework ◽  
Qualitative State ◽  
Core Idea

Time plays an essential role in many biological systems, especially in cell cycle. Many models of biological systems rely on differential equations, but parameter identification is an obstacle to use differential frameworks. In this paper, we present a new hybrid modeling framework that extends René Thomas’ discrete modeling. The core idea is to associate with each qualitative state “celerities” allowing us to compute the time spent in each state. This hybrid framework is illustrated by building a 5-variable model of the mammalian cell cycle. Its parameters are determined by applying formal methods on the underlying discrete model and by constraining parameters using timing observations on the cell cycle. This first hybrid model presents the most important known behaviors of the cell cycle, including quiescent phase and endoreplication.

scholarly journals Inferring cell cycle phases from a partially temporal network of protein interactions

2021 ◽  
Author(s):  
Maxime Lucas ◽  
Arthur Morris ◽  
Alex Townsend-Teague ◽  
Laurent Tichit ◽  
Bianca H Habermann ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Protein Interactions ◽  
Time Series Data ◽  
A Priori ◽  
Biological Systems ◽  
Series Data ◽  
Biological Knowledge ◽  
Temporal Network ◽  
Protein Protein Interactions ◽  
Temporal Organisation

The temporal organisation of biological systems into phases and subphases is often crucial to their functioning. Identifying this multiscale organisation can yield insight into the underlying biological mechanisms at play. To date, however, this identification requires a priori biological knowledge of the system under study. Here, we recover the temporal organisation of the cell cycle of budding yeast into phases and subphases, in an automated way. To do so, we model the cell cycle as a partially temporal network of protein-protein interactions (PPIs) by combining a traditional static PPI network with protein concentration or RNA expression time series data. Then, we cluster the snapshots of this temporal network to infer phases, which show good agreement with our biological knowledge of the cell cycle. We systematically test the robustness of the approach and investigate the effect of having only partial temporal information. The generality of the method makes it suitable for application to other, less well-known biological systems for which the temporal organisation of processes plays an important role.

scholarly journals Boolean factor graph model for biological systems: the yeast cell-cycle network

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Stephen Kotiang ◽  
Ali Eslami
Keyword(s):  
Cell Cycle ◽  
Yeast Cell ◽  
Gene Regulatory Networks ◽  
Biological Networks ◽  
Error Propagation ◽  
Regulatory Networks ◽  
Biological Systems ◽  
Yeast Cell Cycle ◽  
Computational Framework ◽  
Gene Regulatory

Abstract Background The desire to understand genomic functions and the behavior of complex gene regulatory networks has recently been a major research focus in systems biology. As a result, a plethora of computational and modeling tools have been proposed to identify and infer interactions among biological entities. Here, we consider the general question of the effect of perturbation on the global dynamical network behavior as well as error propagation in biological networks to incite research pertaining to intervention strategies. Results This paper introduces a computational framework that combines the formulation of Boolean networks and factor graphs to explore the global dynamical features of biological systems. A message-passing algorithm is proposed for this formalism to evolve network states as messages in the graph. In addition, the mathematical formulation allows us to describe the dynamics and behavior of error propagation in gene regulatory networks by conducting a density evolution (DE) analysis. The model is applied to assess the network state progression and the impact of gene deletion in the budding yeast cell cycle. Simulation results show that our model predictions match published experimental data. Also, our findings reveal that the sample yeast cell-cycle network is not only robust but also consistent with real high-throughput expression data. Finally, our DE analysis serves as a tool to find the optimal values of network parameters for resilience against perturbations, especially in the inference of genetic graphs. Conclusion Our computational framework provides a useful graphical model and analytical tools to study biological networks. It can be a powerful tool to predict the consequences of gene deletions before conducting wet bench experiments because it proves to be a quick route to predicting biologically relevant dynamic properties without tunable kinetic parameters.

scholarly journals Dissipative structures in biological systems: bistability, oscillations, spatial patterns and waves

2018 ◽  
Vol 376 (2124) ◽  
pp. 20170376 ◽  
Author(s):  
Albert Goldbeter
Keyword(s):  
Cell Cycle ◽  
Cell Fate ◽  
Periodic Structures ◽  
Biological Systems ◽  
Dissipative Structures ◽  
Turing Patterns ◽  
Biological Organization ◽  
Wide Range ◽  
Propagating Waves ◽  
Spatio Temporal

The goal of this review article is to assess how relevant is the concept of dissipative structure for understanding the dynamical bases of non-equilibrium self-organization in biological systems, and to see where it has been applied in the five decades since it was initially proposed by Ilya Prigogine. Dissipative structures can be classified into four types, which will be considered, in turn, and illustrated by biological examples: (i) multistability, in the form of bistability and tristability, which involve the coexistence of two or three stable steady states, or in the form of birhythmicity, which involves the coexistence between two stable rhythms; (ii) temporal dissipative structures in the form of sustained oscillations, illustrated by biological rhythms; (iii) spatial dissipative structures, known as Turing patterns; and (iv) spatio-temporal structures in the form of propagating waves. Rhythms occur with widely different periods at all levels of biological organization, from neural, cardiac and metabolic oscillations to circadian clocks and the cell cycle; they play key roles in physiology and in many disorders. New rhythms are being uncovered while artificial ones are produced by synthetic biology. Rhythms provide the richest source of examples of dissipative structures in biological systems. Bistability has been observed experimentally, but has primarily been investigated in theoretical models in an increasingly wide range of biological contexts, from the genetic to the cell and animal population levels, both in physiological conditions and in disease. Bistable transitions have been implicated in the progression between the different phases of the cell cycle and, more generally, in the process of cell fate specification in the developing embryo. Turing patterns are exemplified by the formation of some periodic structures in the course of development and by skin stripe patterns in animals. Spatio-temporal patterns in the form of propagating waves are observed within cells as well as in intercellular communication. This review illustrates how dissipative structures of all sorts abound in biological systems. This article is part of the theme issue ‘Dissipative structures in matter out of equilibrium: from chemistry, photonics and biology (part 1)’.

scholarly journals Boolean Network Model Predicts Cell Cycle Sequence of Fission Yeast

PLoS ONE ◽  
2008 ◽  
Vol 3 (2) ◽  
pp. e1672 ◽  
Author(s):  
Maria I. Davidich ◽  
Stefan Bornholdt
Keyword(s):  
Cell Cycle ◽  
Fission Yeast ◽  
Network Model ◽  
Boolean Network ◽  
Boolean Network Model

scholarly journals Computing Integrated Information (Φ) in Discrete Dynamical Systems with Multi-Valued Elements

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 6
Author(s):  
Juan D. Gomez ◽  
William G. P. Mayner ◽  
Maggie Beheler-Amass ◽  
Giulio Tononi ◽  
Larissa Albantakis
Keyword(s):  
Dynamical Systems ◽  
Software Package ◽  
Causal Structure ◽  
Original System ◽  
Causal Analysis ◽  
Discrete Dynamical Systems ◽  
Random Networks ◽  
Mathematical Framework ◽  
Integrated Information Theory ◽  
Integrated Information

Integrated information theory (IIT) provides a mathematical framework to characterize the cause-effect structure of a physical system and its amount of integrated information (Φ). An accompanying Python software package (“PyPhi”) was recently introduced to implement this framework for the causal analysis of discrete dynamical systems of binary elements. Here, we present an update to PyPhi that extends its applicability to systems constituted of discrete, but multi-valued elements. This allows us to analyze and compare general causal properties of random networks made up of binary, ternary, quaternary, and mixed nodes. Moreover, we apply the developed tools for causal analysis to a simple non-binary regulatory network model (p53-Mdm2) and discuss commonly used binarization methods in light of their capacity to preserve the causal structure of the original system with multi-valued elements.

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