Quantifying the distribution of protein oligomerization degree reflects cellular information capacity

Abstract The generation of information, energy and biomass in living cells involves integrated processes that optimally evolve into complex and robust cellular networks. Protein homo-oligomerization, which is correlated with cooperativity in biology, is one means of scaling the complexity of protein networks. It can play critical roles in determining the sensitivity of genetic regulatory circuits and metabolic pathways. Therefore, understanding the roles of oligomerization may lead to new approaches of probing biological functions. Here, we analyzed the frequency of protein oligomerization degree in the cell proteome of nine different organisms, and then, we asked whether there are design trade-offs between protein oligomerization, information precision and energy costs of protein synthesis. Our results indicate that there is an upper limit for the degree of protein oligomerization, possibly because of the trade-off between cellular resource limitations and the information precision involved in biochemical reaction networks. These findings can explain the principles of cellular architecture design and provide a quantitative tool to scale synthetic biological systems.

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Computational modeling of anthocyanin pathway evolution

10.1101/511089 ◽

2019 ◽

Author(s):

Lucas C. Wheeler ◽

Stacey D. Smith

Keyword(s):

Metabolic Pathways ◽

Flower Color ◽

Complex Phenotype ◽

Pathway Evolution ◽

Anthocyanin Pathway ◽

Trade Offs ◽

Wide Range ◽

Pathway Function ◽

Simple Kinetic Model ◽

New Mutations

AbstractAlteration of metabolic pathways is a key component of the evolution of new phenotypes. Flower color is a striking example of the importance of metabolic evolution in a complex phenotype, wherein shifts in the activity of the underlying pathway lead to a wide range of pigments. Although experimental work has identified common classes of mutations responsible for transitions among colors, we lack a unifying model that relates pathway function and activity to the evolution of distinct pigment phenotypes. One challenge in creating such a model is the branching structure of pigment pathways, which may lead to evolutionary trade-offs due to competition for shared substrates. In order to predict the effects of shifts in enzyme function and activity on pigment production, we created a simple kinetic model of a major plant pigmentaion pathway: the anthocyanin pathway. This model describes the production of the three classes of blue, purple and red anthocyanin pigments, and accordingly, includes multiple branches and substrate competition. We first studied the general behavior of this model using a realistic, functional set of parameters. We then stochastically evolved the pathway toward a defined optimum and and analyzed the patterns of fixed mutations. This approach allowed us to quantify the probability density of trajectories through pathway state space and identify the types and number of changes. Finally, we examine whether the observed trajectories and constraints help to explain experimental observations, i.e., the predominance of mutations which change color by altering the function of branching genes in the pathway. These analyses provide a theoretical framework which can be used to predict the consequences of new mutations in terms of both pigment phenotypes and pleiotropic effects.

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Drone Security

Design Innovation and Network Architecture for the Future Internet - Advances in Web Technologies and Engineering ◽

10.4018/978-1-7998-7646-5.ch013 ◽

2021 ◽

pp. 352-384

Author(s):

Marc Lacoste ◽

David Armand ◽

Fanny Parzysz ◽

Loïc Ferreira ◽

Ghada Arfaoui ◽

...

Keyword(s):

Supply Chain ◽

Network Connectivity ◽

Time Constraints ◽

Security Market ◽

Technical Design ◽

Market Growth ◽

Resource Limitations ◽

Trade Offs ◽

Security Challenges ◽

The Impact

This chapter explores the security challenges of the drone ecosystem. Drones raise significant security and safety concerns, both design-time and run-time (e.g., supply-chain, technical design, standardization). Two broad classes of threats are considered, on drones and using drones (e.g., to attack critical infrastructures or vehicles). They involve both professional and non-professional drones and lead to various types of attacks (e.g., IoT-type vulnerabilities, GPS spoofing, spying, kinetic attacks). Trade-offs involving hardware and software solutions to meet efficiency, resource limitations, and real-time constraints are notably hard to find. So far, protection solutions remain elementary compared to the impact of attacks. Advances in technologies, new use cases (e.g., enhancing network connectivity), and a regulatory framework to overcome existing barriers are decisive factors for sustainable drone security market growth.

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Dissipation in Non-Steady State Regulatory Circuits

Entropy ◽

10.3390/e21121212 ◽

2019 ◽

Vol 21 (12) ◽

pp. 1212

Author(s):

Paulina Szymańska-Rożek ◽

Dario Villamaina ◽

Jacek Miȩkisz ◽

Aleksandra M. Walczak

Keyword(s):

Steady State ◽

Initial Conditions ◽

Limit Function ◽

Environmental Signals ◽

Additional Information ◽

Fast Switching ◽

Regulatory Circuits ◽

Trade Offs ◽

Biological Circuits ◽

Gene Regulatory Circuits

In order to respond to environmental signals, cells often use small molecular circuits to transmit information about their surroundings. Recently, motivated by specific examples in signaling and gene regulation, a body of work has focused on the properties of circuits that function out of equilibrium and dissipate energy. We briefly review the probabilistic measures of information and dissipation and use simple models to discuss and illustrate trade-offs between information and dissipation in biological circuits. We find that circuits with non-steady state initial conditions can transmit more information at small readout delays than steady state circuits. The dissipative cost of this additional information proves marginal compared to the steady state dissipation. Feedback does not significantly increase the transmitted information for out of steady state circuits but does decrease dissipative costs. Lastly, we discuss the case of bursty gene regulatory circuits that, even in the fast switching limit, function out of equilibrium.

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Epistatic Transcription Factor Networks Differentially Modulate Arabidopsis Growth and Defense

Genetics ◽

10.1534/genetics.119.302996 ◽

2019 ◽

Vol 214 (2) ◽

pp. 529-541 ◽

Cited By ~ 2

Author(s):

Baohua Li ◽

Michelle Tang ◽

Céline Caseys ◽

Ayla Nelson ◽

Marium Zhou ◽

...

Keyword(s):

Transcription Factor ◽

Energy Costs ◽

Complex Environment ◽

Simple Correlation ◽

Trade Off ◽

Coordination Model ◽

Trade Offs ◽

High Throughput Phenotyping ◽

Transcription Factor Networks ◽

External Signals

Plants integrate internal and external signals to finely coordinate growth and defense for maximal fitness within a complex environment. A common model suggests that growth and defense show a trade-offs relationship driven by energy costs. However, recent studies suggest that the coordination of growth and defense likely involves more conditional and intricate connections than implied by the trade-off model. To explore how a transcription factor (TF) network may coordinate growth and defense, we used a high-throughput phenotyping approach to measure growth and flowering in a set of single and pairwise mutants previously linked to the aliphatic glucosinolate (GLS) defense pathway. Supporting a link between growth and defense, 17 of the 20 tested defense-associated TFs significantly influenced plant growth and/or flowering time. The TFs’ effects were conditional upon the environment and age of the plant, and more critically varied across the growth and defense phenotypes for a given genotype. In support of the coordination model of growth and defense, the TF mutant’s effects on short-chain aliphatic GLS and growth did not display a simple correlation. We propose that large TF networks integrate internal and external signals and separately modulate growth and the accumulation of the defensive aliphatic GLS.

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Optimal prediction with resource constraints using the information bottleneck

10.1101/2020.04.29.069179 ◽

2020 ◽

Author(s):

Vedant Sachdeva ◽

Thierry Mora ◽

Aleksandra M. Walczak ◽

Stephanie Palmer

Keyword(s):

Resource Constraints ◽

Internal Representation ◽

Motion Prediction ◽

Long Term Memory ◽

Resource Limitations ◽

Information Bottleneck ◽

Trade Offs ◽

Markovian Dynamics ◽

The Future

Responding to stimuli requires that organisms encode information about the external world. Not all parts of the signal are important for behavior, and resource limitations demand that signals be compressed. Prediction of the future input is widely beneficial in many biological systems. We compute the trade-offs between representing the past faithfully and predicting the future for input dynamics with different levels of complexity. For motion prediction, we show that, depending on the parameters in the input dynamics, velocity or position coordinates prove more predictive. We identify the properties of global, transferrable strategies for time-varying stimuli. For non-Markovian dynamics we explore the role of long-term memory of the internal representation. Lastly, we show that prediction in evolutionary population dynamics is linked to clustering allele frequencies into non-overlapping memories, revealing a very different prediction strategy from motion prediction.

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Optimal metabolic states in cells

10.1101/483867 ◽

2018 ◽

Cited By ~ 3

Author(s):

Wolfram Liebermeister

Keyword(s):

Resource Allocation ◽

Metabolic Pathways ◽

Network Models ◽

Cellular Systems ◽

Biochemical Network ◽

Mathematical Form ◽

Cellular Functions ◽

Trade Offs ◽

Metabolic States ◽

Common Framework

AbstractCells need to make an efficient use of metabolites, proteins, energy, membrane space, and time, and resource allocation is also an important aspect of metabolism. How, for example, should cells distribute their protein budget between different cellular functions, e.g. different metabolic pathways, to maximise growth? Cellular resource allocation can be studied by combining biochemical network models with optimality problems that choose metabolic states by their cost and benefit. Various types of resource allocation problems have been proposed. The underlying mechanistic models may describe different cellular systems (e.g. metabolic pathways, networks, or compromises between metabolism and protein production) on different level of detail and using different mathematical formulations (e.g. stoichiometric or kinetic). The optimality problems may use metabolite levels, enzyme levels, or fluxes as variables, assume different cost or benefit functions, and describe different kinds of trade-offs, in which cell variables are either constrained or treated as optimisation objectives. Due to all these differences, optimality problems may be hard to compare or combine. To bring them under one umbrella, I show that they can be derived from a common framework, and that their optimality conditions all show the same mathematical form. This unified view on metabolic optimality problems can be used to justify and combine various modelling approaches and biochemical optimality problems.

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A mechanistic link between cellular trade-offs, gene expression and growth

10.1101/014787 ◽

2015 ◽

Cited By ~ 3

Author(s):

Andrea Y. Weisse ◽

Diego A. Oyarzun ◽

Vincent Danos ◽

Peter S. Swain

Keyword(s):

Gene Expression ◽

Growth Rate ◽

Metabolic Pathways ◽

Mechanistic Model ◽

Living Cells ◽

Coarse Grained ◽

Empirical Relationships ◽

Cellular Energy ◽

Modelling Framework ◽

Trade Offs

Intracellular processes rarely work in isolation but continually interact with the rest of the cell. In microbes, for example, we now know that gene expression across the whole genome typically changes with growth rate. The mechanisms driving such global regulation, however, are not well understood. Here we consider three trade-offs that because of limitations in levels of cellular energy, free ribosomes, and proteins are faced by all living cells and construct a mechanistic model that comprises these trade-offs. Our model couples gene expression with growth rate and growth rate with a growing population of cells. We show that the model recovers Monod's law for the growth of microbes and two other empirical relationships connecting growth rate to the mass fraction of ribosomes. Further, we can explain growth related effects in dosage compensation by paralogs and predict host-circuit interactions in synthetic biology. Simulating competitions between strains, we find that the regulation of metabolic pathways may have evolved not to match expression of enzymes to levels of extracellular substrates in changing environments but rather to balance a trade-off between exploiting one type of nutrient over another. Although coarse-grained, the trade-offs that the model embodies are fundamental, and, as such, our modelling framework has potentially wide application, including in both biotechnology and medicine.

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Energy cost analysis to station keeping for satellite formation type “TerraSAR-X - TanDEM-X”

RUDN Journal of Engineering Researches ◽

10.22363/2312-8143-2019-20-3-220-228 ◽

2019 ◽

Vol 20 (3) ◽

pp. 220-228

Author(s):

Andrey A. Baranov ◽

Nikita V. Chernov

Keyword(s):

Energy Cost ◽

Energy Costs ◽

Relative Configuration ◽

Satellite Formation ◽

Trade Offs ◽

Software Product ◽

Formation Type ◽

The Cost ◽

Fuel Costs ◽

Universal Application

The maintenance of a given configuration of the satellite formation of the “TerraSAR-X - TanDEM-X” type is considered. It is assumed that the master satellite performs only maneuvers to maintain the working orbit, and the slave satellite performs identical maneuvers to maintain the working orbit and additionally performs maneuvers to maintain a given relative configuration of the group. For the working orbit of the master satellite, the dependence of the total characteristic velocity costs for maintaining a large semi-axis, eccentricity, inclination, and their various combinations on the maintenance accuracy is studied. The minimum limits of accuracy at which maneuvering is not required are set for each of the elements. This study is general in nature and allows future missions to be planned, providing the necessary trade-offs between the accuracy of maintenance and the total characteristic speed costs that increase as maintenance accuracy increases. Also, a study of the energy costs of relative maintenance, provided that the engines of the master and slave satellites operate almost the same. It is shown that the relative maintenance requires significantly lower fuel costs, even with the accuracy required in the project. The software product developed for this study is of universal application and will be used to investigate the cost of maintaining a more complex system of four satellites, in which three satellites rotate relative to the base satellite.

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Synthetic gene circuits for metabolic control: design trade-offs and constraints

Journal of The Royal Society Interface ◽

10.1098/rsif.2012.0671 ◽

2013 ◽

Vol 10 (78) ◽

pp. 20120671 ◽

Cited By ~ 43

Author(s):

Diego A. Oyarzún ◽

Guy-Bart V. Stan

Keyword(s):

Steady State ◽

Metabolic Pathways ◽

Design Space ◽

Dynamic Range ◽

Feedback System ◽

Control Circuit ◽

Genetic Circuit ◽

Grand Challenge ◽

Trade Offs ◽

Broad Dynamic Range

A grand challenge in synthetic biology is to push the design of biomolecular circuits from purely genetic constructs towards systems that interface different levels of the cellular machinery, including signalling networks and metabolic pathways. In this paper, we focus on a genetic circuit for feedback regulation of unbranched metabolic pathways. The objective of this feedback system is to dampen the effect of flux perturbations caused by changes in cellular demands or by engineered pathways consuming metabolic intermediates. We consider a mathematical model for a control circuit with an operon architecture, whereby the expression of all pathway enzymes is transcriptionally repressed by the metabolic product. We address the existence and stability of the steady state, the dynamic response of the network under perturbations, and their dependence on common tuneable knobs such as the promoter characteristic and ribosome binding site (RBS) strengths. Our analysis reveals trade-offs between the steady state of the enzymes and the intermediates, together with a separation principle between promoter and RBS design. We show that enzymatic saturation imposes limits on the parameter design space, which must be satisfied to prevent metabolite accumulation and guarantee the stability of the network. The use of promoters with a broad dynamic range and a small leaky expression enlarges the design space. Simulation results with realistic parameter values also suggest that the control circuit can effectively upregulate enzyme production to compensate flux perturbations.

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Using optimal control to understand complex metabolic pathways

10.1101/2020.05.07.082198 ◽

2020 ◽

Author(s):

Nikolaos Tsiantis ◽

Julio R. Banga

Keyword(s):

Optimal Control ◽

Metabolic Pathways ◽

Dynamic Models ◽

Computation Time ◽

Enzyme Activation ◽

Computational Framework ◽

Local Optima ◽

Biochemical Pathways ◽

Trade Offs ◽

Optimality Principles

AbstractBackgroundWe revisit the idea of explaining and predicting dynamics in biochemical pathways from first-principles. A promising approach is to exploit optimality principles that can be justified from an evolutionary perspective. In the context of the cell, several previous studies have explained the dynamics of simple metabolic pathways exploiting optimality principles in combination with dynamic models, i.e. using an optimal control framework. For example, dynamics of gene expression in small metabolic models can be explained assuming that cells have developed optimal adaptation strategies. Most of these works have considered rather simplified representations, such as small linear pathways, or reduced networks with a single branching point.ResultsHere we consider the extension of this approach to more realistic scenarios, i.e. biochemical pathways of arbitrary size and structure. We first show that exploiting optimality principles for these networks poses great challenges due to the complexity of the associated optimal control problems. Second, in order to surmount such challenges, we present a computational framework based on multicriteria optimal control which has been designed with scalability and efficiency in mind, extending several recent methods. This framework includes mechanisms to avoid common pitfalls, such as local optima, unstable solutions or excessive computation time. We illustrate its performance with several case studies considering the central carbon metabolism of S. cerevisiae and B. subtilis. In particular, we consider metabolic dynamics during nutrient shift experiments.ConclusionsWe show how multi-objective optimal control can be used to predict temporal profiles of enzyme activation and metabolite concentrations in complex metabolic pathways. Further, we show how the multicriteria approach allows us to consider general cost/benefit trade-offs that have been likely favored by evolution. In this study we have considered metabolic pathways, but this computational framework can also be applied to analyze the dynamics of other complex pathways, such as signal transduction networks.

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