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Li R, Rozum JC, Quail MM, Qasim MN, Sindi SS, Nobile CJ, Albert R, Hernday AD. Inferring gene regulatory networks using transcriptional profiles as dynamical attractors. PLoS Comput Biol 2023; 19:e1010991. [PMID: 37607190 PMCID: PMC10473541 DOI: 10.1371/journal.pcbi.1010991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 09/01/2023] [Accepted: 07/19/2023] [Indexed: 08/24/2023] Open
Abstract
Genetic regulatory networks (GRNs) regulate the flow of genetic information from the genome to expressed messenger RNAs (mRNAs) and thus are critical to controlling the phenotypic characteristics of cells. Numerous methods exist for profiling mRNA transcript levels and identifying protein-DNA binding interactions at the genome-wide scale. These enable researchers to determine the structure and output of transcriptional regulatory networks, but uncovering the complete structure and regulatory logic of GRNs remains a challenge. The field of GRN inference aims to meet this challenge using computational modeling to derive the structure and logic of GRNs from experimental data and to encode this knowledge in Boolean networks, Bayesian networks, ordinary differential equation (ODE) models, or other modeling frameworks. However, most existing models do not incorporate dynamic transcriptional data since it has historically been less widely available in comparison to "static" transcriptional data. We report the development of an evolutionary algorithm-based ODE modeling approach (named EA) that integrates kinetic transcription data and the theory of attractor matching to infer GRN architecture and regulatory logic. Our method outperformed six leading GRN inference methods, none of which incorporate kinetic transcriptional data, in predicting regulatory connections among TFs when applied to a small-scale engineered synthetic GRN in Saccharomyces cerevisiae. Moreover, we demonstrate the potential of our method to predict unknown transcriptional profiles that would be produced upon genetic perturbation of the GRN governing a two-state cellular phenotypic switch in Candida albicans. We established an iterative refinement strategy to facilitate candidate selection for experimentation; the experimental results in turn provide validation or improvement for the model. In this way, our GRN inference approach can expedite the development of a sophisticated mathematical model that can accurately describe the structure and dynamics of the in vivo GRN.
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Affiliation(s)
- Ruihao Li
- Quantitative and Systems Biology Graduate Program, University of California, Merced, Merced, California, United States of America
| | - Jordan C. Rozum
- Department of Systems Science and Industrial Engineering, Binghamton University (State University of New York), Binghamton, New York, United States of America
| | - Morgan M. Quail
- Quantitative and Systems Biology Graduate Program, University of California, Merced, Merced, California, United States of America
| | - Mohammad N. Qasim
- Quantitative and Systems Biology Graduate Program, University of California, Merced, Merced, California, United States of America
| | - Suzanne S. Sindi
- Department of Applied Mathematics, University of California, Merced, Merced, California, United States of America
| | - Clarissa J. Nobile
- Department of Molecular Cell Biology, University of California, Merced, Merced, California, United States of America
- Health Sciences Research Institute, University of California, Merced, Merced, California, United States of America
| | - Réka Albert
- Department of Physics, Pennsylvania State University, University Park, University Park, Pennsylvania, United States of America
- Department of Biology, Pennsylvania State University, University Park, University Park, Pennsylvania, United States of America
| | - Aaron D. Hernday
- Department of Molecular Cell Biology, University of California, Merced, Merced, California, United States of America
- Health Sciences Research Institute, University of California, Merced, Merced, California, United States of America
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2
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Koopmans L, Youk H. Predictive landscapes hidden beneath biological cellular automata. J Biol Phys 2021; 47:355-369. [PMID: 34739687 PMCID: PMC8603977 DOI: 10.1007/s10867-021-09592-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 10/14/2021] [Indexed: 11/11/2022] Open
Abstract
To celebrate Hans Frauenfelder's achievements, we examine energy(-like) "landscapes" for complex living systems. Energy landscapes summarize all possible dynamics of some physical systems. Energy(-like) landscapes can explain some biomolecular processes, including gene expression and, as Frauenfelder showed, protein folding. But energy-like landscapes and existing frameworks like statistical mechanics seem impractical for describing many living systems. Difficulties stem from living systems being high dimensional, nonlinear, and governed by many, tightly coupled constituents that are noisy. The predominant modeling approach is devising differential equations that are tailored to each living system. This ad hoc approach faces the notorious "parameter problem": models have numerous nonlinear, mathematical functions with unknown parameter values, even for describing just a few intracellular processes. One cannot measure many intracellular parameters or can only measure them as snapshots in time. Another modeling approach uses cellular automata to represent living systems as discrete dynamical systems with binary variables. Quantitative (Hamiltonian-based) rules can dictate cellular automata (e.g., Cellular Potts Model). But numerous biological features, in current practice, are qualitatively described rather than quantitatively (e.g., gene is (highly) expressed or not (highly) expressed). Cellular automata governed by verbal rules are useful representations for living systems and can mitigate the parameter problem. However, they can yield complex dynamics that are difficult to understand because the automata-governing rules are not quantitative and much of the existing mathematical tools and theorems apply to continuous but not discrete dynamical systems. Recent studies found ways to overcome this challenge. These studies either discovered or suggest an existence of predictive "landscapes" whose shapes are described by Lyapunov functions and yield "equations of motion" for a "pseudo-particle." The pseudo-particle represents the entire cellular lattice and moves on the landscape, thereby giving a low-dimensional representation of the cellular automata dynamics. We outline this promising modeling strategy.
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Affiliation(s)
- Lars Koopmans
- Program in Applied Physics, Delft University of Technology, Delft, The Netherlands
- Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Hyun Youk
- Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA.
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3
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Rozum JC, Gómez Tejeda Zañudo J, Gan X, Deritei D, Albert R. Parity and time reversal elucidate both decision-making in empirical models and attractor scaling in critical Boolean networks. SCIENCE ADVANCES 2021; 7:7/29/eabf8124. [PMID: 34272246 PMCID: PMC8284893 DOI: 10.1126/sciadv.abf8124] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 06/03/2021] [Indexed: 05/14/2023]
Abstract
We present new applications of parity inversion and time reversal to the emergence of complex behavior from simple dynamical rules in stochastic discrete models. Our parity-based encoding of causal relationships and time-reversal construction efficiently reveal discrete analogs of stable and unstable manifolds. We demonstrate their predictive power by studying decision-making in systems biology and statistical physics models. These applications underpin a novel attractor identification algorithm implemented for Boolean networks under stochastic dynamics. Its speed enables resolving a long-standing open question of how attractor count in critical random Boolean networks scales with network size and whether the scaling matches biological observations. Via 80-fold improvement in probed network size (N = 16,384), we find the unexpectedly low scaling exponent of 0.12 ± 0.05, approximately one-tenth the analytical upper bound. We demonstrate a general principle: A system's relationship to its time reversal and state-space inversion constrains its repertoire of emergent behaviors.
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Affiliation(s)
- Jordan C Rozum
- Department of Physics, The Pennsylvania State University, University Park, PA 16802, USA.
| | - Jorge Gómez Tejeda Zañudo
- Eli and Edythe L. Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Xiao Gan
- Network Science Institute and Department of Physics, Northeastern University, Boston, MA 02115, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Dávid Deritei
- Department of Molecular Biology, Semmelweis University, Budapest, Hungary
| | - Réka Albert
- Department of Physics, The Pennsylvania State University, University Park, PA 16802, USA.
- Department of Biology, The Pennsylvania State University, University Park, PA 16802, USA
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4
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Maheshwari P, Assmann SM, Albert R. A Guard Cell Abscisic Acid (ABA) Network Model That Captures the Stomatal Resting State. Front Physiol 2020; 11:927. [PMID: 32903539 PMCID: PMC7438572 DOI: 10.3389/fphys.2020.00927] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 07/09/2020] [Indexed: 12/11/2022] Open
Abstract
Stomatal pores play a central role in the control of carbon assimilation and plant water status. The guard cell pair that borders each pore integrates information from environmental and endogenous signals and accordingly swells or deflates, thereby increasing or decreasing the stomatal aperture. Prior research shows that there is a complex cellular network underlying this process. We have previously constructed a signal transduction network and a Boolean dynamic model describing stomatal closure in response to signals including the plant hormone abscisic acid (ABA), calcium or reactive oxygen species (ROS). Here, we improve the Boolean network model such that it captures the biologically expected response of the guard cell in the absence or following the removal of a closure-inducing signal such as ABA or external Ca2+. The expectation from the biological system is reversibility, i.e., the stomata should reopen after the closing signal is removed. We find that the model's reversibility is obstructed by the previously assumed persistent activity of four nodes. By introducing time-dependent Boolean functions for these nodes, the model recapitulates stomatal reopening following the removal of a signal. The previous version of the model predicts ∼20% closure in the absence of any signal due to uncertainty regarding the initial conditions of multiple network nodes. We systematically test and adjust these initial conditions to find the minimally restrictive combinations that appropriately result in open stomata in the absence of a closure signal. We support these results by an analysis of the successive stabilization of feedback motifs in the network, illuminating the system's dynamic progression toward the open or closed stomata state. This analysis particularly highlights the role of cytosolic calcium oscillations in causing and maintaining stomatal closure. Overall, we illustrate the strength of the Boolean network modeling framework to efficiently capture cellular phenotypes as emergent outcomes of intracellular biological processes.
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Affiliation(s)
- Parul Maheshwari
- Department of Physics, Penn State University, University Park, PA, United States
| | - Sarah M. Assmann
- Department of Biology, Penn State University, University Park, PA, United States
| | - Reka Albert
- Department of Physics, Penn State University, University Park, PA, United States
- Department of Biology, Penn State University, University Park, PA, United States
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5
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Logical modeling of cell fate specification—Application to T cell commitment. Curr Top Dev Biol 2020; 139:205-238. [DOI: 10.1016/bs.ctdb.2020.02.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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6
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Sordo Vieira L, Laubenbacher RC, Murrugarra D. Control of Intracellular Molecular Networks Using Algebraic Methods. Bull Math Biol 2019; 82:2. [PMID: 31919596 DOI: 10.1007/s11538-019-00679-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 12/02/2019] [Indexed: 10/25/2022]
Abstract
Many problems in biology and medicine have a control component. Often, the goal might be to modify intracellular networks, such as gene regulatory networks or signaling networks, in order for cells to achieve a certain phenotype, what happens in cancer. If the network is represented by a mathematical model for which mathematical control approaches are available, such as systems of ordinary differential equations, then this problem might be solved systematically. Such approaches are available for some other model types, such as Boolean networks, where structure-based approaches have been developed, as well as stable motif techniques. However, increasingly many published discrete models are mixed-state or multistate, that is, some or all variables have more than two states, and thus the development of control strategies for multistate networks is needed. This paper presents a control approach broadly applicable to general multistate models based on encoding them as polynomial dynamical systems over a finite algebraic state set, and using computational algebra for finding appropriate intervention strategies. To demonstrate the feasibility and applicability of this method, we apply it to a recently developed multistate intracellular model of E2F-mediated bladder cancerous growth and to a model linking intracellular iron metabolism and oncogenic pathways. The control strategies identified for these published models are novel in some cases and represent new hypotheses, or are supported by the literature in others as potential drug targets. Our Macaulay2 scripts to find control strategies are publicly available through GitHub at https://github.com/luissv7/multistatepdscontrol.
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Affiliation(s)
- Luis Sordo Vieira
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, 06032, USA
| | - Reinhard C Laubenbacher
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, 06032, USA.,Center for Quantitative Medicine, UConn Health, Farmington, CT, 06032, USA
| | - David Murrugarra
- Department of Mathematics, University of Kentucky, Lexington, KY, 40506, USA.
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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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Zañudo JGT, Guinn MT, Farquhar K, Szenk M, Steinway SN, Balázsi G, Albert R. Towards control of cellular decision-making networks in the epithelial-to-mesenchymal transition. Phys Biol 2019; 16:031002. [PMID: 30654341 PMCID: PMC6405305 DOI: 10.1088/1478-3975/aaffa1] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
We present the epithelial-to-mesenchymal transition (EMT) from two perspectives: experimental/technological and theoretical. We review the state of the current understanding of the regulatory networks that underlie EMT in three physiological contexts: embryonic development, wound healing, and metastasis. We describe the existing experimental systems and manipulations used to better understand the molecular participants and factors that influence EMT and metastasis. We review the mathematical models of the regulatory networks involved in EMT, with a particular emphasis on the network motifs (such as coupled feedback loops) that can generate intermediate hybrid states between the epithelial and mesenchymal states. Ultimately, the understanding gained about these networks should be translated into methods to control phenotypic outcomes, especially in the context of cancer therapeutic strategies. We present emerging theories of how to drive the dynamics of a network toward a desired dynamical attractor (e.g. an epithelial cell state) and emerging synthetic biology technologies to monitor and control the state of cells.
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Affiliation(s)
- Jorge Gómez Tejeda Zañudo
- Department of Physics, Pennsylvania State University, University Park, PA 16802, USA
- Department of Medical Oncology, Dana-Farber Cancer Center, Boston, MA 02215, USA
- Cancer Program, Eli and Edythe L. Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - M. Tyler Guinn
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY 11794 USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
- Stony Brook Medical Scientist Training Program, 101 Nicolls Road, Stony Brook, NY 11794, USA
| | - Kevin Farquhar
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Mariola Szenk
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY 11794 USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Steven N. Steinway
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Gábor Balázsi
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY 11794 USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Réka Albert
- Department of Physics, Pennsylvania State University, University Park, PA 16802, USA
- Department of Biology, Pennsylvania State University, University Park, PA 16802, USA
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9
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Campbell C, Albert R. Edgetic perturbations to eliminate fixed-point attractors in Boolean regulatory networks. CHAOS (WOODBURY, N.Y.) 2019; 29:023130. [PMID: 30823730 DOI: 10.1063/1.5083060] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Accepted: 01/29/2019] [Indexed: 06/09/2023]
Abstract
The dynamics of complex biological networks may be modeled in a Boolean framework, where the state of each system component is either abundant (ON) or scarce/absent (OFF), and each component's dynamic trajectory is determined by a logical update rule involving the state(s) of its regulator(s). It is possible to encode the update rules in the topology of the so-called expanded graph, analysis of which reveals the long-term behavior, or attractors, of the network. Here, we develop an algorithm to perturb the expanded graph (or, equivalently, the logical update rules) to eliminate stable motifs: subgraphs that cause a subset of components to stabilize to one state. Depending on the topology of the expanded graph, these perturbations lead to the modification or loss of the corresponding attractor. While most perturbations of biological regulatory networks in the literature involve the knockout (fixing to OFF) or constitutive activation (fixing to ON) of one or more nodes, we here consider edgetic perturbations, where a node's update rule is modified such that one or more of its regulators is viewed as ON or OFF regardless of its actual state. We apply the methodology to two biological networks. In a network representing T-LGL leukemia, we identify edgetic perturbations that eliminate the cancerous attractor, leaving only the healthy attractor representing cell death. In a network representing drought-induced closure of plant stomata, we identify edgetic perturbations that modify the single attractor such that stomata, instead of being fixed in the closed state, oscillates between the open and closed states.
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Affiliation(s)
- Colin Campbell
- Department of Physics, Washington College, Chestertown, Maryland 21620, USA
| | - Réka Albert
- Department of Physics, Pennsylvania State University, University Park, Pennsylvania 16802, USA
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10
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Rozum JC, Albert R. Identifying (un)controllable dynamical behavior in complex networks. PLoS Comput Biol 2018; 14:e1006630. [PMID: 30532150 PMCID: PMC6301693 DOI: 10.1371/journal.pcbi.1006630] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Revised: 12/20/2018] [Accepted: 11/11/2018] [Indexed: 12/13/2022] Open
Abstract
We present a technique applicable in any dynamical framework to identify control-robust subsets of an interacting system. These robust subsystems, which we call stable modules, are characterized by constraints on the variables that make up the subsystem. They are robust in the sense that if the defining constraints are satisfied at a given time, they remain satisfied for all later times, regardless of what happens in the rest of the system, and can only be broken if the constrained variables are externally manipulated. We identify stable modules as graph structures in an expanded network, which represents causal links between variable constraints. A stable module represents a system "decision point", or trap subspace. Using the expanded network, small stable modules can be composed sequentially to form larger stable modules that describe dynamics on the system level. Collections of large, mutually exclusive stable modules describe the system's repertoire of long-term behaviors. We implement this technique in a broad class of dynamical systems and illustrate its practical utility via examples and algorithmic analysis of two published biological network models. In the segment polarity gene network of Drosophila melanogaster, we obtain a state-space visualization that reproduces by novel means the four possible cell fates and predicts the outcome of cell transplant experiments. In the T-cell signaling network, we identify six signaling elements that determine the high-signal response and show that control of an element connected to them cannot disrupt this response.
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Affiliation(s)
- Jordan C Rozum
- Department of Physics, The Pennsylvania State University, University Park, PA, USA
| | - Réka Albert
- Department of Physics, The Pennsylvania State University, University Park, PA, USA
- Department of Biology, The Pennsylvania State University, University Park, PA, USA
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11
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Rozum JC, Albert R. Self-sustaining positive feedback loops in discrete and continuous systems. J Theor Biol 2018; 459:36-44. [PMID: 30240578 DOI: 10.1016/j.jtbi.2018.09.017] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2018] [Revised: 08/19/2018] [Accepted: 09/17/2018] [Indexed: 12/29/2022]
Abstract
We consider a dynamic framework frequently used to model gene regulatory and signal transduction networks: monotonic ODEs that are composed of Hill functions. We derive conditions under which activity or inactivity in one system variable induces and sustains activity or inactivity in another. Cycles of such influences correspond to positive feedback loops that are self-sustaining and control-robust, in the sense that these feedback loops "trap" the system in a region of state space from which it cannot exit, even if the other system variables are externally controlled. To demonstrate the utility of this result, we consider prototypical examples of bistability and hysteresis in gene regulatory networks, and analyze a T-cell signal transduction ODE model from the literature.
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Affiliation(s)
- Jordan C Rozum
- Department of Physics, The Pennsylvania State University, University Park, PA, USA.
| | - Réka Albert
- Department of Physics, The Pennsylvania State University, University Park, PA, USA; Department of Biology, The Pennsylvania State University, University Park, PA, USA.
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12
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Naldi A, Hernandez C, Levy N, Stoll G, Monteiro PT, Chaouiya C, Helikar T, Zinovyev A, Calzone L, Cohen-Boulakia S, Thieffry D, Paulevé L. The CoLoMoTo Interactive Notebook: Accessible and Reproducible Computational Analyses for Qualitative Biological Networks. Front Physiol 2018; 9:680. [PMID: 29971009 PMCID: PMC6018415 DOI: 10.3389/fphys.2018.00680] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Accepted: 05/15/2018] [Indexed: 01/07/2023] Open
Abstract
Analysing models of biological networks typically relies on workflows in which different software tools with sensitive parameters are chained together, many times with additional manual steps. The accessibility and reproducibility of such workflows is challenging, as publications often overlook analysis details, and because some of these tools may be difficult to install, and/or have a steep learning curve. The CoLoMoTo Interactive Notebook provides a unified environment to edit, execute, share, and reproduce analyses of qualitative models of biological networks. This framework combines the power of different technologies to ensure repeatability and to reduce users' learning curve of these technologies. The framework is distributed as a Docker image with the tools ready to be run without any installation step besides Docker, and is available on Linux, macOS, and Microsoft Windows. The embedded computational workflows are edited with a Jupyter web interface, enabling the inclusion of textual annotations, along with the explicit code to execute, as well as the visualization of the results. The resulting notebook files can then be shared and re-executed in the same environment. To date, the CoLoMoTo Interactive Notebook provides access to the software tools GINsim, BioLQM, Pint, MaBoSS, and Cell Collective, for the modeling and analysis of Boolean and multi-valued networks. More tools will be included in the future. We developed a Python interface for each of these tools to offer a seamless integration in the Jupyter web interface and ease the chaining of complementary analyses.
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Affiliation(s)
- Aurélien Naldi
- Computational Systems Biology Team, Institut de Biologie de I'Ecole Normale Supérieure, Centre National de la Recherche Scientifique UMR8197, Institut National de la Santé et de la Recherche Médicale U1024, École Normale Supérieure, PSL Université, Paris, France
| | - Céline Hernandez
- Computational Systems Biology Team, Institut de Biologie de I'Ecole Normale Supérieure, Centre National de la Recherche Scientifique UMR8197, Institut National de la Santé et de la Recherche Médicale U1024, École Normale Supérieure, PSL Université, Paris, France
| | - Nicolas Levy
- Laboratoire de Recherche en Informatique UMR8623, Université Paris-Sud, Centre National de la Recherche Scientifique, Université Paris-Saclay, Orsay, France
- École Normale Supérieure de Lyon, Lyon, France
| | - Gautier Stoll
- Université Paris Descartes/Paris V, Sorbonne Paris Cité, Paris, France
- Équipe 11 Labellisée Ligue Nationale Contre le Cancer, Centre de Recherche des Cordeliers, Paris, France
- Institut National de la Santé et de la Recherche Médicale, U1138, Paris, France
- Université Pierre et Marie Curie, Paris, France
- Metabolomics and Cell Biology Platforms, Gustave Roussy Cancer, Villejuif, France
| | - Pedro T. Monteiro
- INESC-ID/Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
| | | | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, Paris, France
- Institut National de la Santé et de la Recherche Médicale, U900, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
- Lobachevsky University, Nizhni Novgorod, Russia
| | - Laurence Calzone
- Institut Curie, PSL Research University, Paris, France
- Institut National de la Santé et de la Recherche Médicale, U900, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | - Sarah Cohen-Boulakia
- Laboratoire de Recherche en Informatique UMR8623, Université Paris-Sud, Centre National de la Recherche Scientifique, Université Paris-Saclay, Orsay, France
| | - Denis Thieffry
- Computational Systems Biology Team, Institut de Biologie de I'Ecole Normale Supérieure, Centre National de la Recherche Scientifique UMR8197, Institut National de la Santé et de la Recherche Médicale U1024, École Normale Supérieure, PSL Université, Paris, France
| | - Loïc Paulevé
- Laboratoire de Recherche en Informatique UMR8623, Université Paris-Sud, Centre National de la Recherche Scientifique, Université Paris-Saclay, Orsay, France
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