Untangling the Hairball: Fitness-Based Asymptotic Reduction of Biological Networks

Proulx-Giraldeau, Félix; Rademaker, Thomas J.; François, Paul

doi:10.1016/j.bpj.2017.08.036

Cited by 17 publications

(12 citation statements)

References 45 publications

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“…2(a). Importantly, we have shown previously that many other biochemical models present similar properties for the steady-state response as a function of the input ligand distribution [35]. In the following, we summarize the most important mathematical properties of such models.…”

Section: A Adaptive Proofreading For Cellular Decision-makingmentioning

confidence: 93%

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Attack and Defense in Cellular Decision-Making: Lessons from Machine Learning

2019

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Machine-learning algorithms can be fooled by small well-designed adversarial perturbations. This is reminiscent of cellular decision-making where ligands (called antagonists) prevent correct signaling, like in early immune recognition. We draw a formal analogy between neural networks used in machine learning and models of cellular decision-making (adaptive proofreading). We apply attacks from machine learning to simple decision-making models and show explicitly the correspondence to antagonism by weakly bound ligands. Such antagonism is absent in more nonlinear models, which inspires us to implement a biomimetic defense in neural networks filtering out adversarial perturbations. We then apply a gradient-descent approach from machine learning to different cellular decision-making models, and we reveal the existence of two regimes characterized by the presence or absence of a critical point for the gradient. This critical point causes the strongest antagonists to lie close to the decision boundary. This is validated in the loss landscapes of robust neural networks and cellular decisionmaking models, and observed experimentally for immune cells. For both regimes, we explain how associated defense mechanisms shape the geometry of the loss landscape and why different adversarial attacks are effective in different regimes. Our work connects evolved cellular decision-making to machine learning and motivates the design of a general theory of adversarial perturbations, both for in vivo and in silico systems.

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Section: A Adaptive Proofreading For Cellular Decision-makingmentioning

confidence: 93%

“…Similar equations for an output T N;m can be derived for many types of networks, as described in Ref. [35]. For this reason we focus in the following on the properties of T N;m , forgetting about the internal biochemistry giving rise to this behavior.…”

Section: A Adaptive Proofreading For Cellular Decision-makingmentioning

confidence: 95%

Attack and Defense in Cellular Decision-Making: Lessons from Machine Learning

2019

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“…To reduce model complexity while keeping close to the details of actual biochemical reactions, one can instead start from a complex biochemical network described by many ordinary differential equations, and "prune" its parameters by setting them, individually or by their combinations (ratio or products), to 0 or infinity [73]. Applying this strategy to a complex model of T cell recognition [74], which contains close to 100 parameters, shows that its behaviour can be boiled down to just three cou-pled differential equations highlighting its main features of adaptation and discrimination, and reveals the broad design principles that implement these features.…”

Section: Coarse-graining Of Molecular Details and Model Reductionmentioning

confidence: 99%

Quantitative Immunology for Physicists

Altan‐Bonnet

Mora

Walczak

2019

Preprint

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The adaptive immune system is a dynamical, self-organized multiscale system that protects vertebrates from both pathogens and internal irregularities, such as tumours. For these reason it fascinates physicists, yet the multitude of different cells, molecules and sub-systems is often also petrifying. Despite this complexity, as experiments on different scales of the adaptive immune system become more quantitative, many physicists have made both theoretical and experimental contributions that help predict the behaviour of ensembles of cells and molecules that participate in an immune response. Here we review some recent contributions with an emphasis on quantitative questions and methodologies. We also provide a more general methods section that presents some of the wide array of theoretical tools used in the field. CONTENTS Dissociation ratesAs discussed below, immunological interactions span the range of very short lived interactions, k off > 10 s −1 or τ off = (k off ) −1 < 0.1 s for antibody binding to its target in the initial phase of an immune response, to extremely long-lived interactions, k off < 10 −4 s −1 or τ off > 3 hours for cytokines interacting with their receptors. These estimates set a huge range of time scales the immune system must deal with, even before taking into consideration delays in cellular responses and how these affect the ligand environment on time scales ranging from hours to days. These considerations form the crux of the matter for quantitative immunology at the cellular scale: while the physical chemistry of ligand-receptor interactions is straightforward, the immune system builds a response of devilish complexity from such elementary interactions. Numbers of receptors per cellIn some situations, such as in T cell antigen recognition, where both the T cell receptor and the antigen exist in a membrane-bound form. All dynamics of interactions must be estimated taking into account the surface concentrations of molecules, with proper adjustment for slower diffusion in the association rates.

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“…Future directions will include automatic model reduction using Fitness Based Asymptotic Parameter reduction ( [ 26 ]), implementation of practical problems such as data fitting, and of networks transitory forms similar to [ 27 ].…”

Section: Availability and Future Directionsmentioning

confidence: 99%

φ-evo: A program to evolve phenotypic models of biological networks

2018

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Molecular networks are at the core of most cellular decisions, but are often difficult to comprehend. Reverse engineering of network architecture from their functions has proved fruitful to classify and predict the structure and function of molecular networks, suggesting new experimental tests and biological predictions. We present φ-evo, an open-source program to evolve in silico phenotypic networks performing a given biological function. We include implementations for evolution of biochemical adaptation, adaptive sorting for immune recognition, metazoan development (somitogenesis, hox patterning), as well as Pareto evolution. We detail the program architecture based on , , and a interface for project configuration and network analysis. We illustrate the predictive power of φ-evo by first recovering the asymmetrical structure of the lac operon regulation from an objective function with symmetrical constraints. Second, we use the problem of hox-like embryonic patterning to show how a single effective fitness can emerge from multi-objective (Pareto) evolution. φ-evo provides an efficient approach and user-friendly interface for the phenotypic prediction of networks and the numerical study of evolution itself.

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Untangling the Hairball: Fitness-Based Asymptotic Reduction of Biological Networks

Cited by 17 publications

References 45 publications

Attack and Defense in Cellular Decision-Making: Lessons from Machine Learning

Attack and Defense in Cellular Decision-Making: Lessons from Machine Learning

Quantitative Immunology for Physicists

φ-evo: A program to evolve phenotypic models of biological networks

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