1
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Varley TF, Bongard J. Evolving higher-order synergies reveals a trade-off between stability and information-integration capacity in complex systems. CHAOS (WOODBURY, N.Y.) 2024; 34:063127. [PMID: 38865092 DOI: 10.1063/5.0200425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 05/21/2024] [Indexed: 06/13/2024]
Abstract
There has recently been an explosion of interest in how "higher-order" structures emerge in complex systems comprised of many interacting elements (often called "synergistic" information). This "emergent" organization has been found in a variety of natural and artificial systems, although at present, the field lacks a unified understanding of what the consequences of higher-order synergies and redundancies are for systems under study. Typical research treats the presence (or absence) of synergistic information as a dependent variable and report changes in the level of synergy in response to some change in the system. Here, we attempt to flip the script: rather than treating higher-order information as a dependent variable, we use evolutionary optimization to evolve boolean networks with significant higher-order redundancies, synergies, or statistical complexity. We then analyze these evolved populations of networks using established tools for characterizing discrete dynamics: the number of attractors, the average transient length, and the Derrida coefficient. We also assess the capacity of the systems to integrate information. We find that high-synergy systems are unstable and chaotic, but with a high capacity to integrate information. In contrast, evolved redundant systems are extremely stable, but have negligible capacity to integrate information. Finally, the complex systems that balance integration and segregation (known as Tononi-Sporns-Edelman complexity) show features of both chaosticity and stability, with a greater capacity to integrate information than the redundant systems while being more stable than the random and synergistic systems. We conclude that there may be a fundamental trade-off between the robustness of a system's dynamics and its capacity to integrate information (which inherently requires flexibility and sensitivity) and that certain kinds of complexity naturally balance this trade-off.
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Affiliation(s)
- Thomas F Varley
- Department of Computer Science, University of Vermont, Burlington, Vermont 05405, USA
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont 05405, USA
| | - Josh Bongard
- Department of Computer Science, University of Vermont, Burlington, Vermont 05405, USA
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont 05405, USA
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2
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Mitra S, Sil P, Subbaroyan A, Martin OC, Samal A. Preponderance of generalized chain functions in reconstructed Boolean models of biological networks. Sci Rep 2024; 14:6734. [PMID: 38509145 PMCID: PMC10954731 DOI: 10.1038/s41598-024-57086-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 03/14/2024] [Indexed: 03/22/2024] Open
Abstract
Boolean networks (BNs) have been extensively used to model gene regulatory networks (GRNs). The dynamics of BNs depend on the network architecture and regulatory logic rules (Boolean functions (BFs)) associated with nodes. Nested canalyzing functions (NCFs) have been shown to be enriched among the BFs in the large-scale studies of reconstructed Boolean models. The central question we address here is whether that enrichment is due to certain sub-types of NCFs. We build on one sub-type of NCFs, the chain functions (or chain-0 functions) proposed by Gat-Viks and Shamir. First, we propose two other sub-types of NCFs, namely, the class of chain-1 functions and generalized chain functions, the union of the chain-0 and chain-1 types. Next, we find that the fraction of NCFs that are chain-0 (also holds for chain-1) functions decreases exponentially with the number of inputs. We provide analytical treatment for this and other observations on BFs. Then, by analyzing three different datasets of reconstructed Boolean models we find that generalized chain functions are significantly enriched within the NCFs. Lastly we illustrate that upon imposing the constraints of generalized chain functions on three different GRNs we are able to obtain biologically viable Boolean models.
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Affiliation(s)
- Suchetana Mitra
- Indian Institute of Science Education and Research (IISER) Mohali, Manauli, Punjab, 140306, India
- The Institute of Mathematical Sciences (IMSc), Chennai, 600113, India
| | - Priyotosh Sil
- The Institute of Mathematical Sciences (IMSc), Chennai, 600113, India
- Homi Bhabha National Institute (HBNI), Mumbai, 400094, India
| | - Ajay Subbaroyan
- The Institute of Mathematical Sciences (IMSc), Chennai, 600113, India
- Homi Bhabha National Institute (HBNI), Mumbai, 400094, India
| | - Olivier C Martin
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91405, Orsay, France.
- Université Paris-Cité, CNRS, INRAE, Institute of Plant Sciences Paris-Saclay (IPS2), 91405, Orsay, France.
| | - Areejit Samal
- The Institute of Mathematical Sciences (IMSc), Chennai, 600113, India.
- Homi Bhabha National Institute (HBNI), Mumbai, 400094, India.
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3
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Tung A, Sperry MM, Clawson W, Pavuluri A, Bulatao S, Yue M, Flores RM, Pai VP, McMillen P, Kuchling F, Levin M. Embryos assist morphogenesis of others through calcium and ATP signaling mechanisms in collective teratogen resistance. Nat Commun 2024; 15:535. [PMID: 38233424 PMCID: PMC10794468 DOI: 10.1038/s41467-023-44522-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 12/17/2023] [Indexed: 01/19/2024] Open
Abstract
Information for organismal patterning can come from a variety of sources. We investigate the possibility that instructive influences for normal embryonic development are provided not only at the level of cells within the embryo, but also via interactions between embryos. To explore this, we challenge groups of embryos with disruptors of normal development while varying group size. Here, we show that Xenopus laevis embryos are much more sensitive to a diverse set of chemical and molecular-biological perturbations when allowed to develop alone or in small groups, than in large groups. Keeping per-embryo exposure constant, we find that increasing the number of exposed embryos in a cohort increases the rate of survival while incidence of defects decreases. This inter-embryo assistance effect is mediated by short-range diffusible signals and involves the P2 ATP receptor. Our data and computational model emphasize that morphogenesis is a collective phenomenon not only at the level of cells, but also of whole bodies, and that cohort size is a crucial variable in studies of ecotoxicology, teratogenesis, and developmental plasticity.
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Affiliation(s)
- Angela Tung
- Allen Discovery Center at Tufts University, Medford, MA, 02155, USA
| | - Megan M Sperry
- Allen Discovery Center at Tufts University, Medford, MA, 02155, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, 02115, USA
| | - Wesley Clawson
- Allen Discovery Center at Tufts University, Medford, MA, 02155, USA
| | - Ananya Pavuluri
- Allen Discovery Center at Tufts University, Medford, MA, 02155, USA
| | - Sydney Bulatao
- Allen Discovery Center at Tufts University, Medford, MA, 02155, USA
| | - Michelle Yue
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, 02115, USA
| | - Ramses Martinez Flores
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, 02115, USA
| | - Vaibhav P Pai
- Allen Discovery Center at Tufts University, Medford, MA, 02155, USA
| | - Patrick McMillen
- Allen Discovery Center at Tufts University, Medford, MA, 02155, USA
| | - Franz Kuchling
- Allen Discovery Center at Tufts University, Medford, MA, 02155, USA
| | - Michael Levin
- Allen Discovery Center at Tufts University, Medford, MA, 02155, USA.
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, 02115, USA.
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4
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Parmer T, Radicchi F. Dynamical methods for target control of biological networks. ROYAL SOCIETY OPEN SCIENCE 2023; 10:230542. [PMID: 37920567 PMCID: PMC10618059 DOI: 10.1098/rsos.230542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 09/29/2023] [Indexed: 11/04/2023]
Abstract
Estimating the influence that individual nodes have on one another in a Boolean network is essential to predict and control the system's dynamical behaviour, for example, detecting key therapeutic targets to control pathways in models of biological signalling and regulation. Exact estimation is generally not possible due to the fact that the number of configurations that must be considered grows exponentially with the system size. However, approximate, scalable methods exist in the literature. These methods can be divided into two main classes: (i) graph-theoretic methods that rely on representations of Boolean dynamics into static graphs and (ii) mean-field approaches that describe average trajectories of the system but neglect dynamical correlations. Here, we compare systematically the performance of these state-of-the-art methods on a large collection of real-world gene regulatory networks. We find comparable performance across methods. All methods underestimate the ground truth, with mean-field approaches having a better recall but a worse precision than graph-theoretic methods. Computationally speaking, graph-theoretic methods are faster than mean-field ones in sparse networks, but are slower in dense networks. The preference of which method to use, therefore, depends on a network's connectivity and the relative importance of recall versus precision for the specific application at hand.
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Affiliation(s)
- Thomas Parmer
- Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47408, USA
| | - Filippo Radicchi
- Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47408, USA
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5
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Manicka S, Johnson K, Levin M, Murrugarra D. The nonlinearity of regulation in biological networks. NPJ Syst Biol Appl 2023; 9:10. [PMID: 37015937 PMCID: PMC10073134 DOI: 10.1038/s41540-023-00273-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 03/23/2023] [Indexed: 04/06/2023] Open
Abstract
The extent to which the components of a biological system are (non)linearly regulated determines how amenable they are to therapy and control. To better understand this property termed "regulatory nonlinearity", we analyzed a suite of 137 published Boolean network models, containing a variety of complex nonlinear regulatory interactions, using a probabilistic generalization of Boolean logic that George Boole himself had proposed. Leveraging the continuous-nature of this formulation, we used Taylor decomposition to approximate the models with various levels of regulatory nonlinearity. A comparison of the resulting series of approximations of the biological models with appropriate random ensembles revealed that biological regulation tends to be less nonlinear than expected, meaning that higher-order interactions among the regulatory inputs tend to be less pronounced. A further categorical analysis of the biological models revealed that the regulatory nonlinearity of cancer and disease networks could not only be sometimes higher than expected but also be relatively more variable. We show that this variation is caused by differences in the apportioning of information among the various orders of regulatory nonlinearity. Our results suggest that there may have been a weak but discernible selection pressure for biological systems to evolve linear regulation on average, but for certain systems such as cancer, on the other hand, to simultaneously evolve more nonlinear rules.
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Affiliation(s)
- Santosh Manicka
- Department of Biology, Tufts University, Medford, MA, 02155, USA
| | - Kathleen Johnson
- Department of Mathematics, University of Kentucky, Lexington, KY, 40506, USA
| | - Michael Levin
- Department of Biology, Tufts University, Medford, MA, 02155, USA
| | - David Murrugarra
- Department of Mathematics, University of Kentucky, Lexington, KY, 40506, USA.
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6
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Costa FX, Rozum JC, Marcus AM, Rocha LM. Effective Connectivity and Bias Entropy Improve Prediction of Dynamical Regime in Automata Networks. ENTROPY (BASEL, SWITZERLAND) 2023; 25:374. [PMID: 36832740 PMCID: PMC9955587 DOI: 10.3390/e25020374] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 02/11/2023] [Accepted: 02/14/2023] [Indexed: 06/18/2023]
Abstract
Biomolecular network dynamics are thought to operate near the critical boundary between ordered and disordered regimes, where large perturbations to a small set of elements neither die out nor spread on average. A biomolecular automaton (e.g., gene, protein) typically has high regulatory redundancy, where small subsets of regulators determine activation via collective canalization. Previous work has shown that effective connectivity, a measure of collective canalization, leads to improved dynamical regime prediction for homogeneous automata networks. We expand this by (i) studying random Boolean networks (RBNs) with heterogeneous in-degree distributions, (ii) considering additional experimentally validated automata network models of biomolecular processes, and (iii) considering new measures of heterogeneity in automata network logic. We found that effective connectivity improves dynamical regime prediction in the models considered; in RBNs, combining effective connectivity with bias entropy further improves the prediction. Our work yields a new understanding of criticality in biomolecular networks that accounts for collective canalization, redundancy, and heterogeneity in the connectivity and logic of their automata models. The strong link we demonstrate between criticality and regulatory redundancy provides a means to modulate the dynamical regime of biochemical networks.
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Affiliation(s)
- Felipe Xavier Costa
- Systems Science and Industrial Engineering Department, Binghamton University (State University of New York), Binghamton, NY 13902, USA
- Instituto Gulbenkian de Ciência, 2780-156 Oeiras, Portugal
- Department of Physics, State University of New York at Albany, Albany, NY 12222, USA
| | - Jordan C. Rozum
- Systems Science and Industrial Engineering Department, Binghamton University (State University of New York), Binghamton, NY 13902, USA
| | - Austin M. Marcus
- Systems Science and Industrial Engineering Department, Binghamton University (State University of New York), Binghamton, NY 13902, USA
| | - Luis M. Rocha
- Systems Science and Industrial Engineering Department, Binghamton University (State University of New York), Binghamton, NY 13902, USA
- Instituto Gulbenkian de Ciência, 2780-156 Oeiras, Portugal
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7
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Brattig Correia R, Barrat A, Rocha LM. Contact networks have small metric backbones that maintain community structure and are primary transmission subgraphs. PLoS Comput Biol 2023; 19:e1010854. [PMID: 36821564 PMCID: PMC9949650 DOI: 10.1371/journal.pcbi.1010854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 01/06/2023] [Indexed: 02/24/2023] Open
Abstract
The structure of social networks strongly affects how different phenomena spread in human society, from the transmission of information to the propagation of contagious diseases. It is well-known that heterogeneous connectivity strongly favors spread, but a precise characterization of the redundancy present in social networks and its effect on the robustness of transmission is still lacking. This gap is addressed by the metric backbone, a weight- and connectivity-preserving subgraph that is sufficient to compute all shortest paths of weighted graphs. This subgraph is obtained via algebraically-principled axioms and does not require statistical sampling based on null-models. We show that the metric backbones of nine contact networks obtained from proximity sensors in a variety of social contexts are generally very small, 49% of the original graph for one and ranging from about 6% to 20% for the others. This reflects a surprising amount of redundancy and reveals that shortest paths on these networks are very robust to random attacks and failures. We also show that the metric backbone preserves the full distribution of shortest paths of the original contact networks-which must include the shortest inter- and intra-community distances that define any community structure-and is a primary subgraph for epidemic transmission based on pure diffusion processes. This suggests that the organization of social contact networks is based on large amounts of shortest-path redundancy which shapes epidemic spread in human populations. Thus, the metric backbone is an important subgraph with regard to epidemic spread, the robustness of social networks, and any communication dynamics that depend on complex network shortest paths.
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Affiliation(s)
- Rion Brattig Correia
- Instituto Gulbenkian de Ciência, Oeiras, Portugal
- Department of Systems Science and Industrial Engineering, Center for Social and Biomedical Complexity, Binghamton University, Binghamton New York, United States of America
| | - Alain Barrat
- Aix Marseille Univ, Université de Toulon, CNRS, CPT, Turing Center for Living Systems, Marseille, France
| | - Luis M. Rocha
- Instituto Gulbenkian de Ciência, Oeiras, Portugal
- Department of Systems Science and Industrial Engineering, Center for Social and Biomedical Complexity, Binghamton University, Binghamton New York, United States of America
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8
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An S, Jang SY, Park SM, Lee CK, Kim HM, Cho KH. Global stabilizing control of large-scale biomolecular regulatory networks. Bioinformatics 2023; 39:6998201. [PMID: 36688702 PMCID: PMC9891247 DOI: 10.1093/bioinformatics/btad045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 01/13/2023] [Accepted: 01/20/2023] [Indexed: 01/24/2023] Open
Abstract
MOTIVATION Cellular behavior is determined by complex non-linear interactions between numerous intracellular molecules that are often represented by Boolean network models. To achieve a desired cellular behavior with minimal intervention, we need to identify optimal control targets that can drive heterogeneous cellular states to the desired phenotypic cellular state with minimal node intervention. Previous attempts to realize such global stabilization were based solely on either network structure information or simple linear dynamics. Other attempts based on non-linear dynamics are not scalable. RESULTS Here, we investigate the underlying relationship between structurally identified control targets and optimal global stabilizing control targets based on non-linear dynamics. We discovered that optimal global stabilizing control targets can be identified by analyzing the dynamics between structurally identified control targets. Utilizing these findings, we developed a scalable global stabilizing control framework using both structural and dynamic information. Our framework narrows down the search space based on strongly connected components and feedback vertex sets then identifies global stabilizing control targets based on the canalization of Boolean network dynamics. We find that the proposed global stabilizing control is superior with respect to the number of control target nodes, scalability, and computational complexity. AVAILABILITY AND IMPLEMENTATION We provide a GitHub repository that contains the DCGS framework written in Python as well as biological random Boolean network datasets (https://github.com/sugyun/DCGS). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | | | - Chun-Kyung Lee
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Hoon-Min Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
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9
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Rocha LM. On the feasibility of dynamical analysis of network models of biochemical regulation. Bioinformatics 2022; 38:3674-3675. [PMID: 35640987 PMCID: PMC9272802 DOI: 10.1093/bioinformatics/btac360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 10/20/2021] [Accepted: 05/27/2022] [Indexed: 11/12/2022] Open
Affiliation(s)
- Luis M Rocha
- Consortium of Social and Biomedical Complexity, Department of Systems Science and Industrial Engineering, Binghamton University (State University of New York), Binghamton, NY 13902, USA
- Instituto Gulbenkian de Ciência, 2780-156 Oeiras, Portugal
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10
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Parmer T, Rocha LM, Radicchi F. Influence maximization in Boolean networks. Nat Commun 2022; 13:3457. [PMID: 35710639 PMCID: PMC9203747 DOI: 10.1038/s41467-022-31066-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 06/01/2022] [Indexed: 11/30/2022] Open
Abstract
The optimization problem aiming at the identification of minimal sets of nodes able to drive the dynamics of Boolean networks toward desired long-term behaviors is central for some applications, as for example the detection of key therapeutic targets to control pathways in models of biological signaling and regulatory networks. Here, we develop a method to solve such an optimization problem taking inspiration from the well-studied problem of influence maximization for spreading processes in social networks. We validate the method on small gene regulatory networks whose dynamical landscapes are known by means of brute-force analysis. We then systematically study a large collection of gene regulatory networks. We find that for about 65% of the analyzed networks, the minimal driver sets contain less than 20% of their nodes.
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Affiliation(s)
- Thomas Parmer
- Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA
| | - Luis M Rocha
- Consortium for Social and Biomedical Complexity, Systems Science and Industrial Engineering Department, Thomas J. Watson College of Engineering and Applied Science, Binghamton University (State University of New York), Binghamton, NY, 13902, USA
- Instituto Gulbenkian de Ciência, Oeiras, 2780-156, Portugal
| | - Filippo Radicchi
- Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA.
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11
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Werle SD, Ikonomi N, Schwab JD, Kraus JM, Weidner FM, Lenhard Rudolph K, Pfister AS, Schuler R, Kühl M, Kestler HA. Identification of dynamic driver sets controlling phenotypical landscapes. Comput Struct Biotechnol J 2022; 20:1603-1617. [PMID: 35465155 PMCID: PMC9010550 DOI: 10.1016/j.csbj.2022.03.034] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 03/30/2022] [Accepted: 03/30/2022] [Indexed: 11/03/2022] Open
Abstract
Controlling phenotypical landscapes is of vital interest to modern biology. This task becomes highly demanding because cellular decisions involve complex networks engaging in crosstalk interactions. Previous work on control theory indicates that small sets of compounds can control single phenotypes. However, a dynamic approach is missing to determine the drivers of the whole network dynamics. By analyzing 35 biologically motivated Boolean networks, we developed a method to identify small sets of compounds sufficient to decide on the entire phenotypical landscape. These compounds do not strictly prefer highly related compounds and show a smaller impact on the stability of the attractor landscape. The dynamic driver sets include many intervention targets and cellular reprogramming drivers in human networks. Finally, by using a new comprehensive model of colorectal cancer, we provide a complete workflow on how to implement our approach to shift from in silico to in vitro guided experiments.
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12
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Çoban H, Kabakçıoğlu A. Proof for Minimum Sensitivity of Nested Canalizing Functions, a Fractal Bound, and Implications for Biology. PHYSICAL REVIEW LETTERS 2022; 128:118101. [PMID: 35363011 DOI: 10.1103/physrevlett.128.118101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 02/07/2022] [Indexed: 06/14/2023]
Abstract
We prove that nested canalizing functions are the minimum-sensitivity Boolean functions for any activity ratio and we determine the functional form of this boundary which has a nontrivial fractal structure. We further observe that the majority of the gene regulatory functions found in known biological networks (submitted to the Cell Collective database) lie on the line of minimum sensitivity which paradoxically remains largely in the unstable regime. Our results provide a quantitative basis for the argument that an evolutionary preference for nested canalizing functions in gene regulation (e.g., for higher robustness) and for plasticity of gene activity are sufficient for concentration of such systems near the "edge of chaos."
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Affiliation(s)
- H Çoban
- Department of Physics, Koç University, Istanbul 34450, Turkey
| | - A Kabakçıoğlu
- Department of Physics, Koç University, Istanbul 34450, Turkey
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13
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Minimal Developmental Computation: A Causal Network Approach to Understand Morphogenetic Pattern Formation. ENTROPY 2022; 24:e24010107. [PMID: 35052133 PMCID: PMC8774453 DOI: 10.3390/e24010107] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 01/05/2022] [Accepted: 01/07/2022] [Indexed: 12/22/2022]
Abstract
What information-processing strategies and general principles are sufficient to enable self-organized morphogenesis in embryogenesis and regeneration? We designed and analyzed a minimal model of self-scaling axial patterning consisting of a cellular network that develops activity patterns within implicitly set bounds. The properties of the cells are determined by internal ‘genetic’ networks with an architecture shared across all cells. We used machine-learning to identify models that enable this virtual mini-embryo to pattern a typical axial gradient while simultaneously sensing the set boundaries within which to develop it from homogeneous conditions—a setting that captures the essence of early embryogenesis. Interestingly, the model revealed several features (such as planar polarity and regenerative re-scaling capacity) for which it was not directly selected, showing how these common biological design principles can emerge as a consequence of simple patterning modes. A novel “causal network” analysis of the best model furthermore revealed that the originally symmetric model dynamically integrates into intercellular causal networks characterized by broken-symmetry, long-range influence and modularity, offering an interpretable macroscale-circuit-based explanation for phenotypic patterning. This work shows how computation could occur in biological development and how machine learning approaches can generate hypotheses and deepen our understanding of how featureless tissues might develop sophisticated patterns—an essential step towards predictive control of morphogenesis in regenerative medicine or synthetic bioengineering contexts. The tools developed here also have the potential to benefit machine learning via new forms of backpropagation and by leveraging the novel distributed self-representation mechanisms to improve robustness and generalization.
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14
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Manicka S, Marques-Pita M, Rocha LM. Effective connectivity determines the critical dynamics of biochemical networks. J R Soc Interface 2022; 19:20210659. [PMID: 35042384 PMCID: PMC8767216 DOI: 10.1098/rsif.2021.0659] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 12/02/2021] [Indexed: 11/12/2022] Open
Abstract
Living systems comprise interacting biochemical components in very large networks. Given their high connectivity, biochemical dynamics are surprisingly not chaotic but quite robust to perturbations-a feature C.H. Waddington named canalization. Because organisms are also flexible enough to evolve, they arguably operate in a critical dynamical regime between order and chaos. The established theory of criticality is based on networks of interacting automata where Boolean truth values model presence/absence of biochemical molecules. The dynamical regime is predicted using network connectivity and node bias (to be on/off) as tuning parameters. Revising this to account for canalization leads to a significant improvement in dynamical regime prediction. The revision is based on effective connectivity, a measure of dynamical redundancy that buffers automata response to some inputs. In both random and experimentally validated systems biology networks, reducing effective connectivity makes living systems operate in stable or critical regimes even though the structure of their biochemical interaction networks predicts them to be chaotic. This suggests that dynamical redundancy may be naturally selected to maintain living systems near critical dynamics, providing both robustness and evolvability. By identifying how dynamics propagates preferably via effective pathways, our approach helps to identify precise ways to design and control network models of biochemical regulation and signalling.
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Affiliation(s)
- Santosh Manicka
- Center for Social and Biomedical Complexity, Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA
- Instituto Gulbenkian de Ciência, 2780-156 Oeiras, Portugal
| | - Manuel Marques-Pita
- Center for Social and Biomedical Complexity, Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA
- Instituto Gulbenkian de Ciência, 2780-156 Oeiras, Portugal
- Universidade Lusófona, CICANT and COPELABS, Campo Grande 388, 1700-097 Lisbon, Portugal
| | - Luis M. Rocha
- Center for Social and Biomedical Complexity, Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA
- Instituto Gulbenkian de Ciência, 2780-156 Oeiras, Portugal
- Binghamton University, State University of New York, Binghamton, NY, USA
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15
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Gates AJ, Brattig Correia R, Wang X, Rocha LM. The effective graph reveals redundancy, canalization, and control pathways in biochemical regulation and signaling. Proc Natl Acad Sci U S A 2021; 118:e2022598118. [PMID: 33737396 PMCID: PMC8000424 DOI: 10.1073/pnas.2022598118] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
The ability to map causal interactions underlying genetic control and cellular signaling has led to increasingly accurate models of the complex biochemical networks that regulate cellular function. These network models provide deep insights into the organization, dynamics, and function of biochemical systems: for example, by revealing genetic control pathways involved in disease. However, the traditional representation of biochemical networks as binary interaction graphs fails to accurately represent an important dynamical feature of these multivariate systems: some pathways propagate control signals much more effectively than do others. Such heterogeneity of interactions reflects canalization-the system is robust to dynamical interventions in redundant pathways but responsive to interventions in effective pathways. Here, we introduce the effective graph, a weighted graph that captures the nonlinear logical redundancy present in biochemical network regulation, signaling, and control. Using 78 experimentally validated models derived from systems biology, we demonstrate that 1) redundant pathways are prevalent in biological models of biochemical regulation, 2) the effective graph provides a probabilistic but precise characterization of multivariate dynamics in a causal graph form, and 3) the effective graph provides an accurate explanation of how dynamical perturbation and control signals, such as those induced by cancer drug therapies, propagate in biochemical pathways. Overall, our results indicate that the effective graph provides an enriched description of the structure and dynamics of networked multivariate causal interactions. We demonstrate that it improves explainability, prediction, and control of complex dynamical systems in general and biochemical regulation in particular.
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Affiliation(s)
- Alexander J Gates
- Network Science Institute, Northeastern University, Boston, MA 02115;
| | - Rion Brattig Correia
- Instituto Gulbenkian de Ciência, 2780-156 Oeiras, Portugal
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Ministry of Education of Brazil, 70040-020 Brasília, DF, Brazil
| | - Xuan Wang
- Center for Social and Biomedical Complexity, Luddy School of Informatics, Computing & Engineering, Indiana University, Bloomington, IN 47408
| | - Luis M Rocha
- Instituto Gulbenkian de Ciência, 2780-156 Oeiras, Portugal;
- Center for Social and Biomedical Complexity, Luddy School of Informatics, Computing & Engineering, Indiana University, Bloomington, IN 47408
- Department of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902
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16
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Gene regulatory networks exhibit several kinds of memory: quantification of memory in biological and random transcriptional networks. iScience 2021; 24:102131. [PMID: 33748699 PMCID: PMC7970124 DOI: 10.1016/j.isci.2021.102131] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 12/09/2020] [Accepted: 01/26/2021] [Indexed: 02/08/2023] Open
Abstract
Gene regulatory networks (GRNs) process important information in developmental biology and biomedicine. A key knowledge gap concerns how their responses change over time. Hypothesizing long-term changes of dynamics induced by transient prior events, we created a computational framework for defining and identifying diverse types of memory in candidate GRNs. We show that GRNs from a wide range of model systems are predicted to possess several types of memory, including Pavlovian conditioning. Associative memory offers an alternative strategy for the biomedical use of powerful drugs with undesirable side effects, and a novel approach to understanding the variability and time-dependent changes of drug action. We find evidence of natural selection favoring GRN memory. Vertebrate GRNs overall exhibit more memory than invertebrate GRNs, and memory is most prevalent in differentiated metazoan cell networks compared with undifferentiated cells. Timed stimuli are a powerful alternative for biomedical control of complex in vivo dynamics without genomic editing or transgenes. Gene regulatory networks' dynamics are modified by transient stimuli GRNs have several different types of memory, including associative conditioning Evolution favored GRN memory, and differentiated cells have the most memory capacity Training GRNs offers a novel biomedical strategy not dependent on genetic rewiring
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17
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Kim H, Muñoz S, Osuna P, Gershenson C. Antifragility Predicts the Robustness and Evolvability of Biological Networks through Multi-Class Classification with a Convolutional Neural Network. ENTROPY 2020; 22:e22090986. [PMID: 33286756 PMCID: PMC7597304 DOI: 10.3390/e22090986] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 09/02/2020] [Indexed: 12/28/2022]
Abstract
Robustness and evolvability are essential properties to the evolution of biological networks. To determine if a biological network is robust and/or evolvable, it is required to compare its functions before and after mutations. However, this sometimes takes a high computational cost as the network size grows. Here, we develop a predictive method to estimate the robustness and evolvability of biological networks without an explicit comparison of functions. We measure antifragility in Boolean network models of biological systems and use this as the predictor. Antifragility occurs when a system benefits from external perturbations. By means of the differences of antifragility between the original and mutated biological networks, we train a convolutional neural network (CNN) and test it to classify the properties of robustness and evolvability. We found that our CNN model successfully classified the properties. Thus, we conclude that our antifragility measure can be used as a predictor of the robustness and evolvability of biological networks.
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Affiliation(s)
- Hyobin Kim
- Biotech Research and Innovation Centre (BRIC), University of Copenhagen (UCPH), 2200 Copenhagen, Denmark;
- Novo Nordisk Foundation Center for Stem Cell Biology, DanStem, Faculty of Health Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Stalin Muñoz
- Institute for Software Technology (IST), Graz University of Technology, 8010 Graz, Austria;
| | - Pamela Osuna
- Faculté des Sciences et Ingénierie, Sorbonne Université, 75005 Paris, France;
| | - Carlos Gershenson
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, CDMX 04510, Mexico
- Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, CDMX 04510, Mexico
- Department of High Performance Computing, ITMO University, 199034 St. Petersburg, Russia
- Correspondence:
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18
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Deritei D, Rozum J, Ravasz Regan E, Albert R. A feedback loop of conditionally stable circuits drives the cell cycle from checkpoint to checkpoint. Sci Rep 2019; 9:16430. [PMID: 31712566 PMCID: PMC6848090 DOI: 10.1038/s41598-019-52725-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 10/22/2019] [Indexed: 12/12/2022] Open
Abstract
We perform logic-based network analysis on a model of the mammalian cell cycle. This model is composed of a Restriction Switch driving cell cycle commitment and a Phase Switch driving mitotic entry and exit. By generalizing the concept of stable motif, i.e., a self-sustaining positive feedback loop that maintains an associated state, we introduce the concept of a conditionally stable motif, the stability of which is contingent on external conditions. We show that the stable motifs of the Phase Switch are contingent on the state of three nodes through which it receives input from the rest of the network. Biologically, these conditions correspond to cell cycle checkpoints. Holding these nodes locked (akin to a checkpoint-free cell) transforms the Phase Switch into an autonomous oscillator that robustly toggles through the cell cycle phases G1, G2 and mitosis. The conditionally stable motifs of the Phase Switch Oscillator are organized into an ordered sequence, such that they serially stabilize each other but also cause their own destabilization. Along the way they channel the dynamics of the module onto a narrow path in state space, lending robustness to the oscillation. Self-destabilizing conditionally stable motifs suggest a general negative feedback mechanism leading to sustained oscillations.
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Affiliation(s)
- Dávid Deritei
- Department of Physics, Pennsylvania State University, University Park, PA, United States of America
- Department of Network and Data Science, Central European University, Budapest, Hungary
| | - Jordan Rozum
- Department of Physics, Pennsylvania State University, University Park, PA, United States of America
| | - Erzsébet Ravasz Regan
- Biochemistry and Molecular Biology, The College of Wooster, Wooster, OH, United States of America
| | - Réka Albert
- Department of Physics, Pennsylvania State University, University Park, PA, United States of America.
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Manicka S, Levin M. The Cognitive Lens: a primer on conceptual tools for analysing information processing in developmental and regenerative morphogenesis. Philos Trans R Soc Lond B Biol Sci 2019; 374:20180369. [PMID: 31006373 PMCID: PMC6553590 DOI: 10.1098/rstb.2018.0369] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/20/2018] [Indexed: 12/31/2022] Open
Abstract
Brains exhibit plasticity, multi-scale integration of information, computation and memory, having evolved by specialization of non-neural cells that already possessed many of the same molecular components and functions. The emerging field of basal cognition provides many examples of decision-making throughout a wide range of non-neural systems. How can biological information processing across scales of size and complexity be quantitatively characterized and exploited in biomedical settings? We use pattern regulation as a context in which to introduce the Cognitive Lens-a strategy using well-established concepts from cognitive and computer science to complement mechanistic investigation in biology. To facilitate the assimilation and application of these approaches across biology, we review tools from various quantitative disciplines, including dynamical systems, information theory and least-action principles. We propose that these tools can be extended beyond neural settings to predict and control systems-level outcomes, and to understand biological patterning as a form of primitive cognition. We hypothesize that a cognitive-level information-processing view of the functions of living systems can complement reductive perspectives, improving efficient top-down control of organism-level outcomes. Exploration of the deep parallels across diverse quantitative paradigms will drive integrative advances in evolutionary biology, regenerative medicine, synthetic bioengineering, cognitive neuroscience and artificial intelligence. This article is part of the theme issue 'Liquid brains, solid brains: How distributed cognitive architectures process information'.
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Affiliation(s)
| | - Michael Levin
- Allen Discovery Center, Tufts University, Medford, MA 02155, USA
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20
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Pentzien T, Puniya BL, Helikar T, Matache MT. Identification of Biologically Essential Nodes via Determinative Power in Logical Models of Cellular Processes. Front Physiol 2018; 9:1185. [PMID: 30233390 PMCID: PMC6127445 DOI: 10.3389/fphys.2018.01185] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2017] [Accepted: 08/07/2018] [Indexed: 12/15/2022] Open
Abstract
A variety of biological networks can be modeled as logical or Boolean networks. However, a simplification of the reality to binary states of the nodes does not ease the difficulty of analyzing the dynamics of large, complex networks, such as signal transduction networks, due to the exponential dependence of the state space on the number of nodes. This paper considers a recently introduced method for finding a fairly small subnetwork, representing a collection of nodes that determine the states of most other nodes with a reasonable level of entropy. The subnetwork contains the most determinative nodes that yield the highest information gain. One of the goals of this paper is to propose an algorithm for finding a suitable subnetwork size. The information gain is quantified by the so-called determinative power of the nodes, which is obtained via the mutual information, a concept originating in information theory. We find the most determinative nodes for 36 network models available in the online database Cell Collective (http://cellcollective.org). We provide statistical information that indicates a weak correlation between the subnetwork size and other variables, such as network size, or maximum and average determinative power of nodes. We observe that the proportion represented by the subnetwork in comparison to the whole network shows a weak tendency to decrease for larger networks. The determinative power of nodes is weakly correlated to the number of outputs of a node, and it appears to be independent of other topological measures such as closeness or betweenness centrality. Once the subnetwork of the most determinative nodes is identified, we generate a biological function analysis of its nodes for some of the 36 networks. The analysis shows that a large fraction of the most determinative nodes are essential and involved in crucial biological functions. The biological pathway analysis of the most determinative nodes shows that they are involved in important disease pathways.
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Affiliation(s)
- Trevor Pentzien
- Department of Mathematics, University of Nebraska at Omaha, Omaha, NE, United States
| | - Bhanwar L. Puniya
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Mihaela T. Matache
- Department of Mathematics, University of Nebraska at Omaha, Omaha, NE, United States
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21
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Correia RB, Gates AJ, Wang X, Rocha LM. CANA: A Python Package for Quantifying Control and Canalization in Boolean Networks. Front Physiol 2018; 9:1046. [PMID: 30154728 PMCID: PMC6102667 DOI: 10.3389/fphys.2018.01046] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Accepted: 07/13/2018] [Indexed: 01/11/2023] Open
Abstract
Logical models offer a simple but powerful means to understand the complex dynamics of biochemical regulation, without the need to estimate kinetic parameters. However, even simple automata components can lead to collective dynamics that are computationally intractable when aggregated into networks. In previous work we demonstrated that automata network models of biochemical regulation are highly canalizing, whereby many variable states and their groupings are redundant (Marques-Pita and Rocha, 2013). The precise charting and measurement of such canalization simplifies these models, making even very large networks amenable to analysis. Moreover, canalization plays an important role in the control, robustness, modularity and criticality of Boolean network dynamics, especially those used to model biochemical regulation (Gates and Rocha, 2016; Gates et al., 2016; Manicka, 2017). Here we describe a new publicly-available Python package that provides the necessary tools to extract, measure, and visualize canalizing redundancy present in Boolean network models. It extracts the pathways most effective in controlling dynamics in these models, including their effective graph and dynamics canalizing map, as well as other tools to uncover minimum sets of control variables.
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Affiliation(s)
- Rion B. Correia
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States
- CAPES Foundation, Ministry of Education of Brazil, Brasília, Brazil
- Instituto Gulbenkian de Ciência, Oeiras, Portugal
| | - Alexander J. Gates
- Center for Complex Networks Research, Northeastern University, Boston, MA, United States
| | - Xuan Wang
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States
| | - Luis M. Rocha
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States
- Instituto Gulbenkian de Ciência, Oeiras, Portugal
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22
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Control of complex networks requires both structure and dynamics. Sci Rep 2016; 6:24456. [PMID: 27087469 PMCID: PMC4834509 DOI: 10.1038/srep24456] [Citation(s) in RCA: 80] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2015] [Accepted: 03/21/2016] [Indexed: 12/22/2022] Open
Abstract
The study of network structure has uncovered signatures of the organization of complex systems. However, there is also a need to understand how to control them; for example, identifying strategies to revert a diseased cell to a healthy state, or a mature cell to a pluripotent state. Two recent methodologies suggest that the controllability of complex systems can be predicted solely from the graph of interactions between variables, without considering their dynamics: structural controllability and minimum dominating sets. We demonstrate that such structure-only methods fail to characterize controllability when dynamics are introduced. We study Boolean network ensembles of network motifs as well as three models of biochemical regulation: the segment polarity network in Drosophila melanogaster, the cell cycle of budding yeast Saccharomyces cerevisiae, and the floral organ arrangement in Arabidopsis thaliana. We demonstrate that structure-only methods both undershoot and overshoot the number and which sets of critical variables best control the dynamics of these models, highlighting the importance of the actual system dynamics in determining control. Our analysis further shows that the logic of automata transition functions, namely how canalizing they are, plays an important role in the extent to which structure predicts dynamics.
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23
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Palma E, Salinas L, Aracena J. Enumeration and extension of non-equivalent deterministic update schedules in Boolean networks. Bioinformatics 2016; 32:722-9. [DOI: 10.1093/bioinformatics/btv628] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2015] [Accepted: 10/25/2015] [Indexed: 11/13/2022] Open
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24
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Chen J, Nolte V, Schlötterer C. Temperature stress mediates decanalization and dominance of gene expression in Drosophila melanogaster. PLoS Genet 2015; 11:e1004883. [PMID: 25719753 PMCID: PMC4342254 DOI: 10.1371/journal.pgen.1004883] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2014] [Accepted: 11/10/2014] [Indexed: 11/18/2022] Open
Abstract
The regulatory architecture of gene expression remains an area of active research. Here, we studied how the interplay of genetic and environmental variation affects gene expression by exposing Drosophila melanogaster strains to four different developmental temperatures. At 18°C we observed almost complete canalization with only very few allelic effects on gene expression. In contrast, at the two temperature extremes, 13°C and 29°C a large number of allelic differences in gene expression were detected due to both cis- and trans-regulatory effects. Allelic differences in gene expression were mainly dominant, but for up to 62% of the genes the dominance swapped between 13 and 29°C. Our results are consistent with stabilizing selection causing buffering of allelic expression variation in non-stressful environments. We propose that decanalization of gene expression in stressful environments is not only central to adaptation, but may also contribute to genetic disorders in human populations.
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Affiliation(s)
- Jun Chen
- Institut für Populationsgenetik, Vienna, Austria
| | - Viola Nolte
- Institut für Populationsgenetik, Vienna, Austria
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25
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Kim MC, Jeong YK, Song M. Investigating the integrated landscape of the intellectual topology of bioinformatics. Scientometrics 2014. [DOI: 10.1007/s11192-014-1417-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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26
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Kim J, Vandamme D, Kim JR, Munoz AG, Kolch W, Cho KH. Robustness and evolvability of the human signaling network. PLoS Comput Biol 2014; 10:e1003763. [PMID: 25077791 PMCID: PMC4117429 DOI: 10.1371/journal.pcbi.1003763] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2013] [Accepted: 06/20/2014] [Indexed: 11/18/2022] Open
Abstract
Biological systems are known to be both robust and evolvable to internal and external perturbations, but what causes these apparently contradictory properties? We used Boolean network modeling and attractor landscape analysis to investigate the evolvability and robustness of the human signaling network. Our results show that the human signaling network can be divided into an evolvable core where perturbations change the attractor landscape in state space, and a robust neighbor where perturbations have no effect on the attractor landscape. Using chemical inhibition and overexpression of nodes, we validated that perturbations affect the evolvable core more strongly than the robust neighbor. We also found that the evolvable core has a distinct network structure, which is enriched in feedback loops, and features a higher degree of scale-freeness and longer path lengths connecting the nodes. In addition, the genes with high evolvability scores are associated with evolvability-related properties such as rapid evolvability, low species broadness, and immunity whereas the genes with high robustness scores are associated with robustness-related properties such as slow evolvability, high species broadness, and oncogenes. Intriguingly, US Food and Drug Administration-approved drug targets have high evolvability scores whereas experimental drug targets have high robustness scores. Biological systems are known to be robust and evolvable to internal mutations and external environmental changes. What causes these apparently contradictory properties? This study shows that the human signaling network can be decomposed into two structurally distinct subgroups of links that provide both evolvability to environmental changes and robustness against internal mutations. The decomposition of the human signaling network reveals an evolutionary design principle of the network, and also facilitates the identification of potential drug targets.
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Affiliation(s)
- Junil Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Yuseong-gu, Daejeon, Republic of Korea
| | - Drieke Vandamme
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
| | - Jeong-Rae Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Yuseong-gu, Daejeon, Republic of Korea
- Department of Mathematics, University of Seoul, Seoul, Republic of Korea
| | | | - Walter Kolch
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
- School of Medicine and Medical Science, University College Dublin, Dublin, Ireland
| | - Kwang-Hyun Cho
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Yuseong-gu, Daejeon, Republic of Korea
- * E-mail:
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27
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Smythies J, Edelstein L, Ramachandran V. Molecular mechanisms for the inheritance of acquired characteristics-exosomes, microRNA shuttling, fear and stress: Lamarck resurrected? Front Genet 2014; 5:133. [PMID: 24860599 PMCID: PMC4030137 DOI: 10.3389/fgene.2014.00133] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2014] [Accepted: 04/24/2014] [Indexed: 12/17/2022] Open
Affiliation(s)
- John Smythies
- Department of Psychology, Center for Brain and Cognition, University of California San Diego, La Jolla, CA, USA
| | | | - Vilayanur Ramachandran
- Department of Psychology, Center for Brain and Cognition, University of California San Diego, La Jolla, CA, USA
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