Stabilisation of Antithetic Control via Molecular Buffering

A key goal in synthetic biology is the construction of molecular circuits that robustly adapt to perturbations. Although many natural systems display perfect adaptation, whereby stationary molecular concentrations are insensitive to perturbations, its de novo engineering has proven elusive. The discovery of the antithetic control motif was a significant step toward a universal mechanism for engineering perfect adaptation. Antithetic control provides perfect adaptation in a wide range of systems, but it can lead to oscillatory dynamics due to loss of stability, and moreover, it can lose perfect adaptation in fast growing cultures. Here, we introduce an extended antithetic control motif that resolves these limitations. We show that molecular buffering, a widely conserved mechanism for homeostatic control in nature, stabilises oscillations and allows for near-perfect adaptation during rapid growth. We study multiple buffering topologies and compare their performance in terms of their stability and adaptation properties. We illustrate the benefits of our proposed strategy in exemplar models for biofuel production and growth rate control in bacterial cultures. Our results provide an improved circuit for robust control of biomolecular systems.

Relationship between the main characteristics of nervous processes with the sodium content in blood of cows depending on the season

The type of higher nervous activity determines individual differences and the body's ability to adapt to changing environmental conditions. The most perfect adaptation is provided by a combination of high strength, mobility and balance of nervous processes. It was found that the strength of nervous processes both in summer and in winter was inversely related only to the sodium content in blood cells (r = -0.57–0.71; p<0.01). The balance of excitation and inhibition processes in the cerebral cortex in summer was inversely correlated with the sodium content in whole blood (r = -0.56; p<0.05) and blood cells (r = -0.64; p <0.01), in winter – in blood serum of cows (r = 0.62; p <0.01). It is proved that in summer the effect of the balance on the sodium content in whole blood was ղ²х = 0.29 (p <0.05), in blood cells – ղ²х = 0.63 (p<0.001), and in winter in blood cells – ղ²х = 0.37 (p<0.05). In summer, the mobility of nervous processes to a greater extent limited the sodium content in blood of cows than in winter. Thus, the effect of this indicator of cortical regulation on the sodium content in whole blood and blood cells in summer reached ղ²х = 0.25–0.35 (p<0.05), and in winter in blood cells – ղ²х = 0.24 (p<0.05).

Ultrasensitivity and bistability in covalent modification cycles with positive autoregulation

Switch-like behaviours in biochemical networks are of fundamental significance in biological signal processing, and exist as two distinct types: ultrasensitivity and bistability. Signalling motifs that can generate these responses are commonly embedded within more complex network topologies, and drive emergent network responses such as robust perfect adaptation (RPA). Here we propose two new models of a reversible covalent-modification cycle with positive autoregulation (PAR) - a motif structure that is thought to be capable of both ultrasensitivity and bistability in different parameter regimes. These new models appeal to a modelling framework that we call complex complete, which accounts fully for the molecular complexities of the underlying signalling mechanisms. Each of the two new models encodes a specific molecular mechanism for PAR. We demonstrate that the modelling simplifications for PAR models that have been used in previous work, which rely on a Michaelian approximation for the enzyme-mediated reactions, are unable to accurately recapitulate the qualitative signalling responses supported by our ‘full’ complex-complete models. Moreover, we show that the parameter regimes in which ultrasensitivity and bistability obtain in the complex-complete framework contradict the predictions made by the Michaelian simplification. Our results highlight the critical importance of accurately incorporating the molecular details of signalling mechanisms into mathematical models when making predictions on the qualitative nature of network performance, and strongly suggest that the Michaelian approximation may be inadequate for many models of enzyme-mediated chemical reactions with added regulations.Author summaryThe Michaelis-Menten equation is ubiquitously, and frequently quite indiscriminately, used to model enzyme-mediated biochemical reactions – including complex cascades of signalling events that feature added regulations such as positive autoregulation (PAR). An influential and well-established model of PAR in a covalent modification cycle, which appeals to a modified version of the Michaelis-Menten equation, has been used previously to suggest that ultrasensitivity can be generated by this signaling motif, and can therefore promote the fundamental signaling response known as robust perfect adaptation (RPA). This simplified Michaelian model of PAR is also known to exhibit bistability in a parameter regime distinct from the ultrasensitivity-promoting regime.Here we study the validity of the Michaelis-Menten equation in models of enzyme-mediated reactions involving additional regulations by proposing two novel models of PAR-regulated covalent modification cycles. These models are constructed within a framework that we call complex-complete – where the full molecular details of the underlying signalling events are considered explicitly, along with all intermediate molecular species. Our extensive computational simulations highlight the inadequacy of the Michaelis-Menten approximation in these models, by revealing the important qualitative and quantitative consequences that accrue to mathematical simplifications of intricate and complex signalling events.

Um ano de transformação e conhecimento

The pandemic that transformed our lives in 2020 brought important reflections on way of seeing the world demanding new skills and behaviors to interact in an environment so common to innovation - virtual environment. In innovation processes, digital transformation that modifies, alters and creates ways of doing things, declares itself and presents itself as facilitating vehicle, also of interpersonal relationships, requiring us to learn to relate in different ways through digital world, using creativity to overcome social, institutional, political, religious, economic obstacles, among others.In science, researchers strive to understand or explain some transformational impacts and seek a perfect adaptation for transferring and exploitation of appropriate knowledge for each eminent need. However, we are still halfway there.Some relevant topics in academia also explored by IJI - International Journal Innovation, such as, innovative entrepreneurship, innovation and learning, innovation and sustainability, internationalization of innovation, innovation systems and digital transformation are now undergoing a new sieve, a new look at understanding of disruptive effects inherent to this theme on transformation and knowledge. A new window has opened in digital age, provided by new world context.In the past, a commonly observed resistance to including innovation in daily processes, and not only in organizational sphere, are now mandatory and our survival in society depends on them. So, what do we see? We can simply call it transformation, but if we broaden a perspective of events in the year 2020, we risk saying that we do live a cotransformation, that is, a rhythmic and continuous, joint and effective learning in creation of value in all global social spheres. It is no longer about transforming a process or creating a system to achieve desired performance, it is about integrating collective, learning by sharing, changing to be able to transform and, all of this, through an increasingly disruptive world.We highlight Moreira, Teixeira and Locatelli (2020) about influences of motivational goals, confirming Schwartz's (2005) theory that groups differ significantly in relative importance they attach to their values, that is, individuals and groups have priorities or axiological hierarchies different from values (Calvosa, Serra, Almeida, 2011). This understanding must support our challenges regarding how to address a co-transformation and transmission of knowledge to future generations, prioritizing care for preservation of our planet, human relations, adaptation of knowledge to current realities and, above all, ability to innovate at any time.As presented by Falaster et al. (2020) it is not for us to assign a mathematical value to life and health or to guide our research by addressing political agendas, nor to distort theory, method and argumentation to suit any situation. What should motivate us, especially in scientific research, is the understanding and strategic responses in times of crisis: our adaptation and compliance, stress with its effects on decision-making on acquisition of knowledge to co-transform and innovate. For this reason, we emphasize that we are in the middle of road because there are still notorious forces that demand permanence of more stable and static social models. Innovation takes on another level. With its disruptive character, it will continue to build future scenarios in improving performance of society's demands (Pol Ville, 2009) through process of cotransformation emphasized here.Efforts and studies aimed at innovation point to relationship networks as important channels for increasing efficiency (Pio, 2020). This is one of evidence regarding the need for a rhythmic and continuous, joint, and effective interconnection for cotransformation. We are not only pointing to innovation as a support for cotransformation, but as something inevitable in practical life of future generations.We must advance how to promote improvements that bring productivity and effectiveness to social relationships through innovation. This will facilitate our insertion in this “new normal” presented in almost all groups of society. We already know that we need to change at a fast pace, but we often get lost in timing of things, that is, the right time for change. Therefore, we must learn to work among diversity, producing new values that take our society to a new level of civility. And all of this translates into different ways of knowing and learning, transmitting, and assimilating, stretching, and making flexible.In fact, it is necessary to identify elements that determine existence of more dynamic capacities, such as, for example, set of behaviors, skills, routines, processes and mechanisms of learning and knowledge governance aimed at cotransformation. Articulation of these elements can result in varying degrees of innovation and dynamism and can manifest themselves in a more intense or more traditional way, where virtual can be more consolidated. Anyway, this is just an indication that, in a cotransformation model, all indicators are reflective (Meirelles Camargo, 2014).Innovations can lead us to co-transformation, reconfiguring activities requiring a greater evolutionary flow of existing capacities and requiring new experiences and management of these more dynamic capacities. Therefore, research must change its perspectives by establishing links between these capacities, to identify and react to innovations that are a contemporary landmark in recent history.The extent of cotransformation depends on some factors, such as perceived environmental pressure (Helfat et al., 2007) and adaptability (Madjdi Hüsig, 2011) that vary in degree, from small adaptations to major revisions or even a reconfiguration (O'Reilly Tushman, 2008). Generally, we know they are interconnected components that specify how we can survive all dynamism and disruption that exists in the world.In this context, the most important thing is not to know what will be the results of what you want, but to intensely take advantage of construction path of what you can have with appropriate use of capabilities to co-transform and innovate.We hope that in this editorial we have promoted important reflections on understanding of cotransformation, inspiring our readers about new knowledge and expanding a debate for better direction of academic and business society. The role of knowledge and transformation awakens a world of possibilities to be explored, which is why we are still halfway there.

Genetically Engineered Integral Feedback Controllers for Robust Perfect Adaptation in Mammalian Cells

Mammalian cells collectively maintain a consistent internal milieu that supports their host’s survival in varying and uncertain environments. This homeostasis is often achieved through negative feedback loops that act at various levels of biological organization, from the system and organ levels down to gene expression at the molecular scale. Recently, a molecular regulatory motif has been discovered that enables a regulated variable to adapt perfectly (at the steady state) to network and parameter changes and to persistent environmental perturbations. The regulatory motif that achieves this robust perfect adaptation property realizes integral feedback, a control strategy that employs mathematical integration in a negative feedback loop. Here, we present the first synthetic implementation of integral feedback in mammalian cells. We show that this implementation successfully maintains constant levels of a transcription factor, even when its degradation is significantly increased. Furthermore, we establish the structural robustness properties of our controlled system by demonstrating that perturbing the network topology does not affect the transcription factor levels. We believe that the ability to robustly and predictably regulate the expression levels of genes will both become an indispensable tool for basic research as well as lead to substantial advances in the development of industrial biotechnology and cell-based therapies.

An endoribonuclease-based feedforward controller for decoupling resource-limited genetic modules in mammalian cells

Synthetic biology has the potential to bring forth advanced genetic devices for applications in healthcare and biotechnology. However, accurately predicting the behavior of engineered genetic devices remains difficult due to lack of modularity, wherein a device’s output does not depend only on its intended inputs but also on its context. One contributor to lack of modularity is loading of transcriptional and translational resources, which can induce coupling among otherwise independently-regulated genes. Here, we quantify the effects of resource loading in engineered mammalian genetic systems and develop an endoribonuclease-based feedforward controller that can adapt the expression level of a gene of interest to significant resource loading in mammalian cells. Near-perfect adaptation to resource loads is facilitated by high production and catalytic rates of the endoribonuclease. Our design is portable across cell lines and enables predictable tuning of controller function. Ultimately, our controller is a general-purpose device for predictable, robust, and context-independent control of gene expression.

Stability and Control of Biomolecular Circuits through Structure

Due to omnipresent uncertainties and environmental disturbances, natural and engineered biological organisms face the challenging control problem of achieving robust performance using unreliable parts. The key to overcoming this challenge rests in identifying structures of biomolecular circuits that are largely invariant despite uncertainties, and building feedback control through such structures. In this work, we develop the tool of log derivatives to capture structures in how the production and degradation rates of molecules depend on concentrations of reactants. We show that log derivatives could establish stability of fixed points based on structure, despite large variations in rates and functional forms of models. Furthermore, we demonstrate how control objectives, such as robust perfect adaptation (i.e. step disturbance rejection), could be implemented through the structures captured. Due to the method’s simplicity, structural properties for analysis and design of biomolecular circuits can often be determined by a glance at the equations.