Evolution of taxis responses in virtual bacteria: non-adaptive dynamics

An Embodied Intelligence-Based Biologically Inspired Strategy for Searching a Moving Target

Bacterial chemotaxis in unicellular Escherichia coli, the simplest biological creature, enables it to perform effective searching behaviour even with a single sensor, achieved via a sequence of "tumbling" and "swimming" behaviours guided by gradient information. Recent studies show that suitable random walk strategies may guide the behaviour in the absence of gradient information. This article presents a novel and minimalistic biologically inspired search strategy inspired by bacterial chemotaxis and embodied intelligence concept: a concept stating that intelligent behaviour is a result of the interaction among the "brain," body morphology including the sensory sensitivity tuned by the morphology, and the environment. Specifically, we present bacterial chemotaxis inspired searching behaviour with and without gradient information based on biological fluctuation framework: a mathematical framework that explains how biological creatures utilize noises in their behaviour. Via extensive simulation of a single sensor mobile robot that searches for a moving target, we will demonstrate how the effectiveness of the search depends on the sensory sensitivity and the inherent random walk strategies produced by the brain of the robot, comprising Ballistic, Levy, Brownian, and Stationary search. The result demonstrates the importance of embodied intelligence even in a behaviour inspired by the simplest creature.

Bacteria and cells as alternative nano-carriers for biomedical applications

INTRODUCTION

Nano-based systems have received a lot of attention owing to their particular properties and, hence, have been proposed for a wide variety of biomedical applications. These nanosystems could be potentially employed for diagnosis and therapy of different medical issues. Although these nanomaterials are designed for specific tasks, interactions, and transformations when administered to the human body affect their performance and behavior. In this regard, bacteria and other cells have been presented as alternative nanocarriers. These microorganisms can be genetically modified and customized for a more specific therapeutic action and, in combination with nanomaterials, can lead to bio-hybrids with a unique potential for biomedical purposes.

AREAS COVERED

Literature regarding bacteria and cells employed in combination with nanomaterials for biomedical applications is revised and discussed in this review. The potential as well as the limitations of these novel bio-hybrid systems are evaluated. Several examples are presented to show the performance of these alternative nanocarriers.

EXPERT OPINION

Bio-hybrid systems have shown their potential as alternative nanocarriers as they contribute to better performance than traditional nano-based systems. Nevertheless, their limitations must be studied, and advantages and drawbacks assessed before their application to medicine.

Optimization of Nanoparticles for Smart Drug Delivery: A Review

Nanoparticle delivery systems have good application prospects in the treatment of various diseases, especially in cancer treatment. The effect of drug delivery is regulated by the properties of nanoparticles. There have been many studies focusing on optimizing the structure of nanoparticles in recent years, and a series of achievements have been made. This review summarizes the optimization strategies of nanoparticles from three aspects-improving biocompatibility, increasing the targeting efficiency of nanoparticles, and improving the drug loading rate of nanoparticles-aiming to provide some theoretical reference for the subsequent drug delivery of nanoparticles.

Cell primitive-based biomimetic functional materials for enhanced cancer therapy

Cell primitive-based functional materials that combine the advantages of natural substances and nanotechnology have emerged as attractive therapeutic agents for cancer therapy. Cell primitives are characterized by distinctive biological functions, such as long-term circulation, tumor specific targeting, immune modulation etc. Moreover, synthetic nanomaterials featuring unique physical/chemical properties have been widely used as effective drug delivery vehicles or anticancer agents to treat cancer. The combination of these two kinds of materials will catalyze the generation of innovative biomaterials with multiple functions, high biocompatibility and negligible immunogenicity for precise cancer therapy. In this review, we summarize the most recent advances in the development of cell primitive-based functional materials for cancer therapy. Different cell primitives, including bacteria, phages, cells, cell membranes, and other bioactive substances are introduced with their unique bioactive functions, and strategies in combining with synthetic materials, especially nanoparticulate systems, for the construction of function-enhanced biomaterials are also summarized. Furthermore, foreseeable challenges and future perspectives are also included for the future research direction in this field.

Recent trends and advances in microbe-based drug delivery systems

Since more than a decade, pharmaceutical researchers endeavor to develop an effective, safe and target-specific drug delivery system to potentiate the therapeutic actions and reduce the side effects. The conventional drug delivery systems (DDSs) show the improvement in the lifestyle of the patients suffering from non-communicable diseases, autoimmune diseases but sometimes, drug resistance developed during the treatment is a major concern for clinicians to find an alternative and more advanced transport systems. Advancements in drug delivery facilitate the development of active carrier for targeted action with improved pharmacokinetic behavior. This review article focuses on microbe-based drug delivery systems to provide safe, non-toxic, site-specific targeted action with lesser side effects. Pharmaceutical researchers play a vital part in microbe-based drug delivery systems as a therapeutic agent and carrier. The properties of microorganisms like self-propulsion, in-situ production of therapeutics, penetration into the tumor cells, increase in immunity, etc. are of interest for development of highly effective delivery carrier. Lactococcus lactis is therapeutically helpful in Inflammatory Bowel Disease (IBD) and is under investigation of phase I clinical trial. Moreover, bacteria, anti-cancer oncolytic viruses, viral vectors (gene therapy) and viral immunotherapy are the attractive areas of biotechnological research. Virus acts as a distinctive candidate for imaging of tumor and accumulation of active in tumor. Graphical abstract Classification of microbe-based drug delivery system.

Optimal chemotactic responses in stochastic environments

Although the “adaptive” strategy used by Escherichia coli has dominated our understanding of bacterial chemotaxis, the environmental conditions under which this strategy emerged is still poorly understood. In this work, we study the performance of various chemotactic strategies under a range of stochastic time- and space-varying attractant distributions in silico. We describe a novel “speculator” response in which the bacterium compare the current attractant concentration to the long-term average; if it is higher then they tumble persistently, while if it is lower than the average, bacteria swim away in search of more favorable conditions. We demonstrate how this response explains the experimental behavior of aerobically-grown Rhodobacter sphaeroides and that under spatially complex but slowly-changing nutrient conditions the speculator response is as effective as the adaptive strategy of E. coli.

Bioengineered and biohybrid bacteria-based systems for drug delivery

The use of bacterial cells as agents of medical therapy has a long history. Research that was ignited over a century ago with the accidental infection of cancer patients has matured into a platform technology that offers the promise of opening up new potential frontiers in medical treatment. Bacterial cells exhibit unique characteristics that make them well-suited as smart drug delivery agents. Our ability to genetically manipulate the molecular machinery of these cells enables the customization of their therapeutic action as well as its precise tuning and spatio-temporal control, allowing for the design of unique, complex therapeutic functions, unmatched by current drug delivery systems. Early results have been promising, but there are still many important challenges that must be addressed. We present a review of promises and challenges of employing bioengineered bacteria in drug delivery systems and introduce the biohybrid design concept as a new additional paradigm in bacteria-based drug delivery.

The role of regulation in the origin and synthetic modelling of minimal cognition

In this paper we address the question of minimal cognition by investigating the origin of some crucial cognitive properties from the very basic organisation of biological systems. More specifically, we propose a theoretical model of how a system can distinguish between specific features of its interaction with the environment, which is a fundamental requirement for the emergence of minimal forms of cognition. We argue that the appearance of this capacity is grounded in the molecular domain, and originates from basic mechanisms of biological regulation. In doing so, our aim is to provide a theoretical account that can also work as a possible conceptual bridge between Synthetic Biology and Artificial Intelligence. In fact, we argue, Synthetic Biology can contribute to the study of minimal cognition (and therefore to a minimal AI), by providing a privileged approach to the study of these mechanisms by means of artificial systems.

BioJazz: in silico evolution of cellular networks with unbounded complexity using rule-based modeling

Systems biologists aim to decipher the structure and dynamics of signaling and regulatory networks underpinning cellular responses; synthetic biologists can use this insight to alter existing networks or engineer de novo ones. Both tasks will benefit from an understanding of which structural and dynamic features of networks can emerge from evolutionary processes, through which intermediary steps these arise, and whether they embody general design principles. As natural evolution at the level of network dynamics is difficult to study, in silico evolution of network models can provide important insights. However, current tools used for in silico evolution of network dynamics are limited to ad hoc computer simulations and models. Here we introduce BioJazz, an extendable, user-friendly tool for simulating the evolution of dynamic biochemical networks. Unlike previous tools for in silico evolution, BioJazz allows for the evolution of cellular networks with unbounded complexity by combining rule-based modeling with an encoding of networks that is akin to a genome. We show that BioJazz can be used to implement biologically realistic selective pressures and allows exploration of the space of network architectures and dynamics that implement prescribed physiological functions. BioJazz is provided as an open-source tool to facilitate its further development and use. Source code and user manuals are available at: http://oss-lab.github.io/biojazz and http://osslab.lifesci.warwick.ac.uk/BioJazz.aspx.

A minimal model for metabolism-dependent chemotaxis in Rhodobacter sphaeroides (†)

Chemotaxis is vital cellular movement in response to environmental chemicals. Unlike the canonical chemotactic pathway in Escherichia coli, Rhodobacter sphaeroides has both transmembrane and cytoplasmic sensory clusters, with the latter possibly interacting with essential components in the electron transport system. However, the effect of the cytoplasmic sensor and the mechanism of signal integration from both sensory clusters remain unclear. Based on a minimal model of the chemotaxis pathway in this species, we show that signal integration at the motor level produces realistic chemotactic behaviour in line with experimental observations. Our model also suggests that the core pathway of R. sphaeroides, at least its ancestor, may represent a metabolism-dependent selective stopping strategy, which alone can steer cells to favourable environments. Our results not only clarify the potential roles of the two sensory clusters but also put in question the current definitions of attractants and repellents.

Microbial evolution in vivo and in silico: methods and applications

Microbial evolution has been extensively studied in the past fifty years, which has lead to seminal discoveries that have shaped our understanding of evolutionary forces and dynamics. It is only recently however, that transformative technologies and computational advances have enabled a larger in-scale and in-depth investigation of the genetic basis and mechanistic underpinnings of evolutionary adaptation. In this review we focus on the strengths and limitations of in vivo and in silico techniques for studying microbial evolution in the laboratory, and we discuss how these complementary approaches can be integrated in a unifying framework for elucidating microbial evolution.

Bacterial chemotaxis: introverted or extroverted? A comparison of the advantages and disadvantages of basic forms of metabolism-based and metabolism-independent behavior using a computational model

Using a minimal model of metabolism, we examine the limitations of behavior that is (a) solely in response to environmental phenomena or (b) solely in response to metabolic dynamics, showing that basic forms of each of these kinds of behavior are incapable of driving survival-prolonging behavior in certain situations. Inspired by experimental evidence of concurrent metabolism-based and metabolism-independent chemotactic mechanisms in Escherichia coli and Rhodobacter sphaeroides, we then investigate how metabolism-independent and metabolism-based sensitivities can be integrated into a single behavioral response, demonstrating that a simple switching mechanism can be sufficient to effectively integrate metabolism-based and metabolism-independent behaviors. Finally, we use a spatial simulation of bacteria to show that the investigated forms of behavior produce different spatio-temporal patterns that are influenced by the metabolic-history of the bacteria. We suggest that these patterns could be a way to experimentally derive insight into the relationship between metabolism and chemotaxis in real bacteria.

Evolutionary systems biology: what it is and why it matters

Evolutionary systems biology (ESB) is a rapidly growing integrative approach that has the core aim of generating mechanistic and evolutionary understanding of genotype-phenotype relationships at multiple levels. ESB's more specific objectives include extending knowledge gained from model organisms to non-model organisms, predicting the effects of mutations, and defining the core network structures and dynamics that have evolved to cause particular intracellular and intercellular responses. By combining mathematical, molecular, and cellular approaches to evolution, ESB adds new insights and methods to the modern evolutionary synthesis, and offers ways in which to enhance its explanatory and predictive capacities. This combination of prediction and explanation marks ESB out as a research manifesto that goes further than its two contributing fields. Here, we summarize ESB via an analysis of characteristic research examples and exploratory questions, while also making a case for why these integrative efforts are worth pursuing.

Split histidine kinases enable ultrasensitivity and bistability in two-component signaling networks

Bacteria sense and respond to their environment through signaling cascades generally referred to as two-component signaling networks. These networks comprise histidine kinases and their cognate response regulators. Histidine kinases have a number of biochemical activities: ATP binding, autophosphorylation, the ability to act as a phosphodonor for their response regulators, and in many cases the ability to catalyze the hydrolytic dephosphorylation of their response regulator. Here, we explore the functional role of "split kinases" where the ATP binding and phosphotransfer activities of a conventional histidine kinase are split onto two distinct proteins that form a complex. We find that this unusual configuration can enable ultrasensitivity and bistability in the signal-response relationship of the resulting system. These dynamics are displayed under a wide parameter range but only when specific biochemical requirements are met. We experimentally show that one of these requirements, namely segregation of the phosphatase activity predominantly onto the free form of one of the proteins making up the split kinase, is met in Rhodobacter sphaeroides. These findings indicate split kinases as a bacterial alternative for enabling ultrasensitivity and bistability in signaling networks. Genomic analyses reveal that up 1.7% of all identified histidine kinases have the potential to be split and bifunctional.

Evolution of associative learning in chemical networks

Organisms that can learn about their environment and modify their behaviour appropriately during their lifetime are more likely to survive and reproduce than organisms that do not. While associative learning - the ability to detect correlated features of the environment - has been studied extensively in nervous systems, where the underlying mechanisms are reasonably well understood, mechanisms within single cells that could allow associative learning have received little attention. Here, using in silico evolution of chemical networks, we show that there exists a diversity of remarkably simple and plausible chemical solutions to the associative learning problem, the simplest of which uses only one core chemical reaction. We then asked to what extent a linear combination of chemical concentrations in the network could approximate the ideal Bayesian posterior of an environment given the stimulus history so far? This Bayesian analysis revealed the 'memory traces' of the chemical network. The implication of this paper is that there is little reason to believe that a lack of suitable phenotypic variation would prevent associative learning from evolving in cell signalling, metabolic, gene regulatory, or a mixture of these networks in cells.

Phenotypic models of evolution and development: geometry as destiny

Quantitative models of development that consider all relevant genes typically are difficult to fit to embryonic data alone and have many redundant parameters. Computational evolution supplies models of phenotype with relatively few variables and parameters that allows the patterning dynamics to be reduced to a geometrical picture for how the state of a cell moves. The clock and wavefront model, that defines the phenotype of somitogenesis, can be represented as a sequence of two discrete dynamical transitions (bifurcations). The expression-time to space map for Hox genes and the posterior dominance rule are phenotypes that naturally follow from computational evolution without considering the genetics of Hox regulation.

Pareto evolution of gene networks: an algorithm to optimize multiple fitness objectives

The computational evolution of gene networks functions like a forward genetic screen to generate, without preconceptions, all networks that can be assembled from a defined list of parts to implement a given function. Frequently networks are subject to multiple design criteria that cannot all be optimized simultaneously. To explore how these tradeoffs interact with evolution, we implement Pareto optimization in the context of gene network evolution. In response to a temporal pulse of a signal, we evolve networks whose output turns on slowly after the pulse begins, and shuts down rapidly when the pulse terminates. The best performing networks under our conditions do not fall into categories such as feed forward and negative feedback that also encode the input-output relation we used for selection. Pareto evolution can more efficiently search the space of networks than optimization based on a single ad hoc combination of the design criteria.

Evolutionary principles underlying structure and response dynamics of cellular networks

The network view in systems biology, in conjunction with the continuing development of experimental technologies, is providing us with the key structural and dynamical features of both cell-wide and pathway-level regulatory, signaling and metabolic systems. These include for example modularity and presence of hub proteins at the structural level and ultrasensitivity and feedback control at the level of dynamics. The uncovering of such features, and the seeming commonality of some of them, makes many systems biologists believe that these could represent design principles that underpin cellular systems across organisms. Here, we argue that such claims on any observed feature requires an understanding of how it has emerged in evolution and how it can shape subsequent evolution. We review recent and past studies that aim to achieve such evolutionary understanding for observed features of cellular networks. We argue that this evolutionary framework could lead to deciphering evolutionary origin and relevance of proposed design principles, thereby allowing to predict their presence or absence in an organism based on its environment and biochemistry and their effect on its future evolution.

Behavioral metabolution: the adaptive and evolutionary potential of metabolism-based chemotaxis

We use a minimal model of metabolism-based chemotaxis to show how a coupling between metabolism and behavior can affect evolutionary dynamics in a process we refer to as behavioral metabolution. This mutual influence can function as an in-the-moment, intrinsic evaluation of the adaptive value of a novel situation, such as an encounter with a compound that activates new metabolic pathways. Our model demonstrates how changes to metabolic pathways can lead to improvement of behavioral strategies, and conversely, how behavior can contribute to the exploration and fixation of new metabolic pathways. These examples indicate the potentially important role that the interplay between behavior and metabolism could have played in shaping adaptive evolution in early life and protolife. We argue that the processes illustrated by these models can be interpreted as an unorthodox instantiation of the principles of evolution by random variation and selective retention. We then discuss how the interaction between metabolism and behavior can facilitate evolution through (i) increasing exposure to environmental variation, (ii) making more likely the fixation of some beneficial metabolic pathways, (iii) providing a mechanism for in-the-moment adaptation to changes in the environment and to changes in the organization of the organism itself, and (iv) generating conditions that are conducive to speciation.

Evolution of response dynamics underlying bacterial chemotaxis

Background

The ability to predict the function and structure of complex molecular mechanisms underlying cellular behaviour is one of the main aims of systems biology. To achieve it, we need to understand the evolutionary routes leading to a specific response dynamics that can underlie a given function and how biophysical and environmental factors affect which route is taken. Here, we apply such an evolutionary approach to the bacterial chemotaxis pathway, which is documented to display considerable complexity and diversity.

Results

We construct evolutionarily accessible response dynamics starting from a linear response to absolute levels of attractant, to those observed in current-day Escherichia coli. We explicitly consider bacterial movement as a two-state process composed of non-instantaneous tumbling and swimming modes. We find that a linear response to attractant results in significant chemotaxis when sensitivity to attractant is low and when time spent tumbling is large. More importantly, such linear response is optimal in a regime where signalling has low sensitivity. As sensitivity increases, an adaptive response as seen in Escherichia coli becomes optimal and leads to 'perfect' chemotaxis with a low tumbling time. We find that as tumbling time decreases and sensitivity increases, there exist a parameter regime where the chemotaxis performance of the linear and adaptive responses overlap, suggesting that evolution of chemotaxis responses might provide an example for the principle of functional change in structural continuity.

Conclusions

Our findings explain several results from diverse bacteria and lead to testable predictions regarding chemotaxis responses evolved in bacteria living under different biophysical constraints and with specific motility machinery. Further, they shed light on the potential evolutionary paths for the evolution of complex behaviours from simpler ones in incremental fashion.

The roots of bioinformatics in theoretical biology

From the late 1980s onward, the term "bioinformatics" mostly has been used to refer to computational methods for comparative analysis of genome data. However, the term was originally more widely defined as the study of informatic processes in biotic systems. In this essay, I will trace this early history (from a personal point of view) and I will argue that the original meaning of the term is re-emerging.

A minimal model of metabolism-based chemotaxis

Since the pioneering work by Julius Adler in the 1960's, bacterial chemotaxis has been predominantly studied as metabolism-independent. All available simulation models of bacterial chemotaxis endorse this assumption. Recent studies have shown, however, that many metabolism-dependent chemotactic patterns occur in bacteria. We hereby present the simplest artificial protocell model capable of performing metabolism-based chemotaxis. The model serves as a proof of concept to show how even the simplest metabolism can sustain chemotactic patterns of varying sophistication. It also reproduces a set of phenomena that have recently attracted attention on bacterial chemotaxis and provides insights about alternative mechanisms that could instantiate them. We conclude that relaxing the metabolism-independent assumption provides important theoretical advances, forces us to rethink some established pre-conceptions and may help us better understand unexplored and poorly understood aspects of bacterial chemotaxis.

De novo evolution of complex, global and hierarchical gene regulatory mechanisms

Gene regulatory networks exhibit complex, hierarchical features such as global regulation and network motifs. There is much debate about whether the evolutionary origins of such features are the results of adaptation, or the by-products of non-adaptive processes of DNA replication. The lack of availability of gene regulatory networks of ancestor species on evolutionary timescales makes this a particularly difficult problem to resolve. Digital organisms, however, can be used to provide a complete evolutionary record of lineages. We use a biologically realistic evolutionary model that includes gene expression, regulation, metabolism and biosynthesis, to investigate the evolution of complex function in gene regulatory networks. We discover that: (i) network architecture and complexity evolve in response to environmental complexity, (ii) global gene regulation is selected for in complex environments, (iii) complex, inter-connected, hierarchical structures evolve in stages, with energy regulation preceding stress responses, and stress responses preceding growth rate adaptations and (iv) robustness of evolved models to mutations depends on hierarchical level: energy regulation and stress responses tend not to be robust to mutations, whereas growth rate adaptations are more robust and non-lethal when mutated. These results highlight the adaptive and incremental evolution of complex biological networks, and the value and potential of studying realistic in silico evolutionary systems as a way of understanding living systems.

The promise of evolutionary systems biology: lessons from bacterial chemotaxis

Bacterial chemotaxis and the signaling networks underlying it provide us with a model system for studying the molecular basis of behavior and information processing. Although chemotaxis is well characterized at both the phenotype and genotype levels in the model organism Escherichia coli, it is not yet possible to predict chemotaxis behavior in diverse bacteria on the basis of their environment or genome sequence. Moreover, we still cannot propose a plausible evolutionary trajectory from minimal systems to present-day chemotaxis networks. The analysis of all sequenced bacterial genomes provides a prediction of their chemotaxis networks and reveals substantial structural diversity. Additionally, it uncovers a set of previously unknown proteins that could be the "missing" link between complex present-day chemotaxis networks and simpler, ancestral systems composed of a few proteins. Further evaluation of these findings with experimental and modeling studies will allow us to distill evolutionary design principles in chemotaxis signaling networks.

Origins and diversification of a complex signal transduction system in prokaryotes

The molecular machinery that controls chemotaxis in bacteria is substantially more complex than any other signal transduction system in prokaryotes, and its origins and variability among living species are unknown. We found that this multiprotein "chemotaxis system" is present in most prokaryotic species and evolved from simpler two-component regulatory systems that control prokaryotic transcription. We discovered, through genomic analysis, signaling systems intermediate between two-component systems and chemotaxis systems. Evolutionary genomics established central and auxiliary components of the chemotaxis system. While tracing its evolutionary history, we also developed a classification scheme that revealed more than a dozen distinct classes of chemotaxis systems, enabling future predictive modeling of chemotactic behavior in unstudied species.

Coupling metabolism and chemotaxis-dependent behaviours by energy taxis receptors

Bacteria have evolved the ability to monitor changes in various physico-chemical parameters and to adapt their physiology and metabolism by implementing appropriate cellular responses to these changes. Energy taxis is a metabolism-dependent form of taxis and is the directed movement of motile bacteria in gradients of physico-chemical parameters that affect metabolism. Energy taxis has been described in diverse bacterial species and several dedicated energy sensors have been identified. The molecular mechanism of energy taxis has not been studied in as much detail as chemotaxis, but experimental evidence indicates that this behaviour differs from metabolism-independent taxis only by the presence of dedicated energy taxis receptors. Energy taxis receptors perceive changes in energy-related parameters, including signals related to the redox and/or intracellular energy status of the cell. The best-characterized energy taxis receptors are those that sense the redox state of the electron transport chain via non-covalently bound FAD cofactors. Other receptors shown to mediate energy taxis lack any recognizable redox cofactor or conserved energy-sensing motif, and some have been suggested to monitor changes in the proton motive force. The exact energy-sensing mechanism(s) involved are yet to be elucidated for most of these energy sensors. By monitoring changes in energy-related parameters, energy taxis receptors allow cells to couple motility behaviour with metabolism under diverse environmental conditions. Energy taxis receptors thus provide fruitful models to decipher how cells integrate sensory behaviours with metabolic activities.

The evolutionary origins of gene regulation

One way that organisms cope with constantly changing physical and biological conditions is by regulating the expression of genes and thereby altering protein production. Clearly, altering the protein production to match the environmental demands can be adaptive, but there may be evolutionary barriers to the transition from constitutive expression to regulated expression. In particular, down-regulating a gene when it is not needed means that there will necessarily be a delay in protein production when the protein is up-regulated in the future. We develop a model of simple gene regulation in response to randomly changing environmental conditions. We calculate the long-term behavior of gene expression and determine the fitness consequences of changes in the gene regulation. We then embed this model into a population genetic framework in order to determine the conditions that allow populations to evolve environment-specific transcription rates. The population genetic model follows the evolutionary transition from constitutive expression to regulated expression. There are three distinct possible evolutionary outcomes. The gene may be stuck in the always on position, the gene may first evolve to an intermediate constitutive expression level and then evolve regulation, or regulation can evolve directly from the ancestral state in a smooth fashion. Regulation is most likely to evolve when the costs of mis-expression are low and the transcript decay rate is high. This suggests that genes that have less severe reductions in fitness when mis-expressed are more likely to initially evolve regulation.

Reverse engineering of bacterial chemotaxis pathway via frequency domain analysis

Chemotaxis is defined as a behavior involving organisms sensing attractants or repellents and leading towards or away from them. Therefore, it is possible to reengineer chemotaxis network to control the movement of bacteria to our advantage. Understanding the design principles of chemotaxis pathway is a prerequisite and an important topic in synthetic biology. Here, we provide guidelines for chemotaxis pathway design by employing control theory and reverse engineering concept on pathway dynamic design. We first analyzed the mathematical models for two most important kinds of E. coli chemotaxis pathway—adaptive and non-adaptive pathways, and concluded that the control units of the pathway de facto function as a band-pass filter and a low-pass filter, respectively, by abstracting the frequency response properties of the pathways. The advantage of the band-pass filter is established, and we demonstrate how to tune the three key parameters of it—A (max amplification), ω₁ (down cut-off frequency) and ω₂ (up cut-off frequency) to optimize the chemotactic effect. Finally, we hypothesized a similar but simpler version of the dynamic pathway model based on the principles discovered and show that it leads to similar properties with native E. coli chemotactic behaviors. Our study provides an example of simulating and designing biological dynamics in silico and indicates how to make use of the native pathway's features in this process. Furthermore, the characteristics we discovered and tested through reverse engineering may help to understand the design principles of the pathway and promote the design of artificial chemotaxis pathways.

Bacterial strategies for chemotaxis response

Regular environmental conditions allow for the evolution of specifically adapted responses, whereas complex environments usually lead to conflicting requirements upon the organism's response. A relevant instance of these issues is bacterial chemotaxis, where the evolutionary and functional reasons for the experimentally observed response to chemoattractants remain a riddle. Sensing and motility requirements are in fact optimized by different responses, which strongly depend on the chemoattractant environmental profiles. It is not clear then how those conflicting requirements quantitatively combine and compromise in shaping the chemotaxis response. Here we show that the experimental bacterial response corresponds to the maximin strategy that ensures the highest minimum uptake of chemoattractants for any profile of concentration. We show that the maximin response is the unique one that always outcompetes motile but nonchemotactic bacteria. The maximin strategy is adapted to the variable environments experienced by bacteria, and we explicitly show its emergence in simulations of bacterial populations in a chemostat. Finally, we recast the contrast of evolution in regular vs. complex environments in terms of minimax vs. maximin game-theoretical strategies. Our results are generally relevant to biological optimization principles and provide a systematic possibility to get around the need to know precisely the statistics of environmental fluctuations.