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T
TRILL: Orchestrating Modular Deep-Learning Workflows for Democratized, Scalable Protein Analysis and Engineering +
Deep-learning models have been rapidly adopted by many fields, partly due to the deluge of data humanity has amassed. In particular, the petabases of biological sequencing data enable the unsupervised training of protein language models that learn the “language of life.” However, due to their prohibitive size and complexity, contemporary deep-learning models are often unwieldy, especially for scientists with limited machine learning backgrounds. TRILL (TRaining and Inference using the Language of Life) is a platform for creative protein design and discovery. Leveraging several state-of-the-art models such as ESM-2, DiffDock, and RFDiffusion, TRILL allows researchers to generate novel proteins, predict 3-D structures, extract high-dimensional representations of proteins, functionally classify proteins and more. What sets TRILL apart is its ability to enable complex pipelines by chaining together models and effectively merging the capabilities of different models to achieve a sum greater than its individual parts. Whether using Google Colab with one GPU or a supercomputer with hundreds, TRILL allows scientists to effectively utilize models with millions to billions of parameters by using optimized training strategies such as ZeRO-Offload and distributed data parallel. Therefore, TRILL not only bridges the gap between complex deep-learning models and their practical application in the field of biology, but also simplifies the orchestration of these models into comprehensive workflows, democratizing access to powerful methods. +
Delays in gene networks result from the sequential nature of protein assembly. However, it is unclear how models of gene networks that use delays should be modified when considering time-dependent changes in temperature. This is important, as delay is often used in models of genetic oscillators that can be entrained by periodic fluctuations in temperature. Here, we analytically derive the time dependence of delay distributions in response to time-varying temperature changes. We find that the resulting time-varying delay is nonlinearly dependent on parameters of the time-varying temperature such as amplitude and frequency, therefore, applying an Arrhenius scaling may result in erroneous conclusions. We use these results to examine a model of a synthetic gene oscillator with temperature compensation. We show that temperature entrainment follows from the same mechanism that results in temperature compensation. Under a common Arrhenius scaling alone, the frequency of the oscillator is sensitive to changes in the mean temperature but robust to changes in the frequency of a periodically time-varying temperature. When a mechanism for temperature compensation is included in the model, however, we show that the oscillator is entrained by periodically varying temperature even when maintaining insensitivity to the mean temperature. +
P
Population diversification in a yeast metabolic program promotes anticipation of environmental shifts +
Delineating the strategies by which cells contend with combinatorial changing environments is crucial for understanding cellular regulatory organization. When presented with two carbon sources, microorganisms first consume the carbon substrate that supports the highest growth rate (e.g. glucose) and then switch to the secondary carbon source (e.g. galactose), a paradigm known as the Monod model. Sequential sugar utilization has been attributed to transcriptional repression of the secondary metabolic pathway, followed by activation of this pathway upon depletion of the preferred carbon source. In this work, we challenge this notion. Although Saccharomyces cerevisiae cells consume glucose before galactose, we demonstrate that the galactose regulatory pathway is activated in a fraction of the cell population hours before glucose is fully consumed. This early activation reduces the time required for the population to transition between the two metabolic programs and provides a fitness advantage that might be crucial in competitive environments. Importantly, these findings define a new paradigm for the response of microbial populations to combinatorial carbon sources. +
C
Deriving system-level specifications from component specifications usually involves the elimination of variables that are not part of the interface of the top-level system. This paper presents algorithms for eliminating variables from formulas by computing refinements or relaxations of these formulas in a context. We discuss a connection between this problem and optimization and give efficient algorithms to compute refinements and relaxations of linear inequality constraints. +
A
Designing genetic circuits to control the behaviors of microbial populations is an ongoing challenge in synthetic biology. Here we analyze circuits which implement dosage control by controlling levels of a global signal in a microbial population in face of varying cell density, growth rate, and environmental dilution. We utilize the Lux quorum sensing system to implement dosage control circuits, and we analyze the dynamics of circuits using both simplified analytical analysis and in silico simulations. We demonstrate that strong negative feedback through inhibiting LuxI synthase expression along with AiiA degradase activity results in circuits with fast response times and robustness to cell density and dilution rate. We find that degradase activity yields robustness to variations in population density for large population sizes, while negative feedback to synthase production decreases sensitivity to dilution rates. +
C
Designing synthetic microbial consortia is an emerging area in synthetic biology and a major goal is to realize stable and robust coexistence of multiple species. Cooperation and competition are fundamental intra/interspecies interactions that shape population level behaviors, yet it is not well-understood how these interactions affect the stability and robustness of coexistence. In this paper, we show that communities with cooperative interactions are more robust to population disturbance, e.g., depletion by antibiotics, by forming intermixed spatial patterns. Meanwhile, competition leads to population spatial heterogeneity and more fragile coexistence in communities. Using reaction-diffusion and nonlocal PDE models and simulations of a two-species E. coli consortium, we demonstrate that cooperation is more beneficial than competition in maintaining coexistence in spatially structured consortia, but not in well-mixed environments. This also suggests a trade-off between constructing heterogeneous communities with localized functions and maintaining robust coexistence. The results provide general strategies for engineering spatially structured consortia by designing interspecies interactions and suggest the importance of cooperation for biodiversity in microbial community. +
F
Flow-Based Synthesis of Reactive Tests for Discrete Decision-Making Systems with Temporal Logic Specifications +
Designing tests to evaluate if a given autonomous system satisfies complex specifications is challenging due to the complexity of these systems. This work proposes a flow-based approach for reactive test synthesis from temporal logic specifications, enabling the synthesis of test environments consisting of static and reactive obstacles and dynamic test agents. The temporal logic specifications describe desired test behavior, including system requirements as well as a test objective that is not revealed to the system. The synthesized test strategy places restrictions on system actions in reaction to the system state. The tests are minimally restrictive and accomplish the test objective while ensuring realizability of the system's objective without aiding it (semi-cooperative setting). Automata theory and flow networks are leveraged to formulate a mixed-integer linear program (MILP) to synthesize the test strategy. For a dynamic test agent, the agent strategy is synthesized for a GR(1) specification constructed from the solution of the MILP. If the specification is unrealizable by the dynamics of the test agent, a counterexample-guided approach is used to resolve the MILP until a strategy is found. This flow-based, reactive test synthesis is conducted offline and is agnostic to the system controller. Finally, the resulting test strategy is demonstrated in simulation and experimentally on a pair of quadrupedal robots for a variety of specifications. +
O
On-chip membrane protein cell-free expression enables development of a direct binding assay: A curious case of potassium channel KcsA-Kv1.3 +
Despite the significant role integral membrane proteins (IMPs) play in the drug discovery process, it remains extremely challenging to express, purify, and in vitro stabilize them for detailed biophysical analyses. Cell-free transcription-translation systems have emerged as a promising alternative for producing complex proteins, but they are still not a viable option for expressing IMPs due to improper post-translational folding of these proteins. We have studied key factors influencing in vitro folding of cell-free-expressed IMPs, particularly oligomeric proteins (i.e., ion channels). Using a chimeric ion channel, KcsA-Kv1.3 (K-K), as a model IMP, we have investigated several physiochemical determinants including artificial bilayer environments (i.e., lipid, detergent) for K-K in vitro stabilization. We observed that fusion of a ‘superfolder’ green fluorescent protein (sfGFP) to K-K as a protein expression reporter not only improves the protein yield, but surprisingly facilitates the K-K tetramer formation, probably by enhancing the solubility of monomeric K-K. Additionally, anionic lipids (i.e., DMPG) were found to be essential for the correct folding of cell-free-expressed monomeric K-K into tetramer, underscoring the importance of lipid-protein interaction in maintaining structural-functional integrity of ion channels. We further developed methods to integrate cell-free-expressed IMPs directly onto a biosensor chip. We employed a solid-supported lipid bilayer onto the surface plasmon resonance (SPR) chip to insert nascent K-K in a membrane. In a different approach, an anti-GFP-functionalized surface was used to capture in situ expressed K-K via its sfGFP tag. Interestingly, only the K-K-functionalized capture surface prepared by the latter strategy was able to interact with K-K's small binding partners. This generalizable approach can be further extended to other membrane proteins for developing direct binding assays involving small ligands. +
Development of feasible G&C (guidance and control) methods for precision atmospheric re-entry has remained a challenge since pre-Apollo-era space exploration. The inherent difficulty arises from the governing hypersonic dynamics being significantly nonlinear, subject to parametric uncertainty, and limited with control authority. Vehicle safety requirements impose further constraints, and desired cost objectives complicate an already difficult G&C problem. The scope of this paper is to present a guidance algorithm for optimal trajectory generation based on a reduced-dimension reentry formulation. Preliminary simulations demonstrate the algorithm with feedback used to track the guidance trajectory in the presence of initial state uncertainty. The objective is to further this approach toward an onboard receding-horizon implementation +
G
Differential geometry and nonlinear control theory provide essential
tools for studying motion generation in robot systems. Two areas
where progress is being made are motion planning for mobile robots on
the factory floor (or on the surface of Mars), and control of highly
articulated robots---such as multifingered robot hands and robot
``snakes''---for medical inspection and manipulation inside the
gastrointestinal tract. A common feature of these systems is the role
of constraints on the behavior of the system. Typically, these
constraints force the instantaneous velocities of the system to lie in
a restricted set of directions, but do not actually restrict the
reachable configurations of the system. A familiar example in which
this geometric structure can be exploited is parallel parking of an
automobile, where periodic motion in the driving speed and steering
angle can be used to achieve a net sideways motion. By studying the
geometric nature of velocity constraints in a more general setting, it
is possible to synthesize gaits for snake-like robots, generate
parking and docking maneuvers for automated vehicles, and study the
effects of rolling contacts on multifingered robot hands. As in
parallel parking, rectification of periodic motions in the control
variables plays a central role in the techniques which are used to
generation motion in this broad class of robot systems. +
D
Differentially flat systems are underdetermined systems of (nonlinear) ordinary differential equations
(ODEs) whose solution curves are in smooth one-one correspondence with arbitrary curves in a space
whose dimension equals the number of equations by which the system is underdetermined. For control
systems this is the same as the number of inputs. The components of the map from the system space to
the smaller dimensional space are referred to as the flat outputs. Flatness allows one to systematically
generate feasible trajectories in a relatively simple way. Typically the flat outputs may depend on the
original independent and dependent variables in terms of which the ODEs are written as well as finitely
many derivatives of the dependent variables. Flatness of systems underdetermined by one equation is
completely characterised by Elie Cartan's work. But for general underdetermined systems no complete
characterisation of flatness exists.
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In this dissertation we describe two different geometric frameworks for studying flatness and provide
constructive methods for deciding the flatness of certain classes of nonlinear systems and for finding
these flat outputs if they exist. We first introduce the concept of ``absolute equivalence'' due to Cartan
and define flatness in this frame work. We provide a method of testing for the flatness of systems, which
involves making a guess for all but one of the flat outputs after which the problem is reduced to the case
solved by Cartan. Secondly we present an alternative geometric approach to flatness which uses ``jet
bundles'' and present a theorem which partially characterises flat outputs that depend only on the
original variables but not on their derivatives, for the case of systems described by two independent
one-forms in arbitrary number of variables. Finally, for the class of Lagrangian mechanical systems whose
number of control inputs is one less than the number of degrees of freedom, we provide a
characterisation of flat outputs that depend only on the configuration variables, but not on their
derivatives. This characterisation makes use of the Riemannian metric provided by the kinetic energy of
the system.
A
Distributed algorithms for averaging have attracted interest in the control and sensing literature. However, previous works have not addressed some practical concerns that will arise in actual implementations on packet-switched communication networks..... +
D
Distributed algorithms for averaging have at-
tracted interest in the control and sensing literature. However,
previous works have not addressed some practical concerns
that will arise in actual implementations on packet-switched
communication networks such as the Internet. In this paper,
we present several implementable algorithms that are robust to
asynchronism and dynamic topology changes. The algorithms
do not require global coordination and can be proven to
converge under very general asynchronous timing assumptions.
Our results are verified by both simulation and experiments on
a real-world TCP/IP network. +
A
Due to the increasing complexity of space missions and distance to exploration targets, future robotic systems used for space exploration call for more resilience and autonomy. Instead of minimizing the failure risk, we are focusing on missions that will inevitably encounter significant failures and are developing an algorithm that will autonomously reconfigure the system controller to continue to make progress towards the mission goal despite being in a reduced capacity state - we call this extreme resilience. In this paper, we develop a model-free framework to autonomously react to locomotion failures of robotic systems. This is done by the use of a neural network for path planning using the neuroevolution of aug- menting topologies (NEAT) algorithm and a dynamic database of possible moves and their effect on the system’s position and orientation. Two modes of failure detection and resolution are being introduced: (a) relative position failure detection, which is triggered by large, unexpected moves and results in a complete update of the database before a retraining of the neural network, and (b) absolute position failure detection, which triggers from large build-ups of position error from small failures and will induce a retraining of the neural network without an explicit database reset. We implement and validate this framework on a high-fidelity planetary rover simulation using Unreal Engine and on a hardware setup of a TurtleBot2 with a PhantomX Pincher robot arm. +
Engineered bacterial sensors have potential applications in human health monitoring, environmental chemical detection, and materials biosynthesis. While such bacterial devices have long been engineered to differentiate between combinations of inputs, their potential to process signal timing and duration has been overlooked. In this work, we present a two-input temporal logic gate that can sense and record the order of the inputs, the timing between inputs, and the duration of input pulses. Our temporal logic gate design relies on unidirectional DNA recombination mediated by bacteriophage integrases to detect and encode sequences of input events. For an E. coli strain engineered to contain our temporal logic gate, we compare predictions of Markov model simulations with laboratory measurements of final population distributions for both step and pulse inputs. Although single cells were engineered to have digital outputs, stochastic noise created heterogeneous single-cell responses that translated into analog population responses. Furthermore, when single-cell genetic states were aggregated into population-level distributions, these distributions contained unique information not encoded in individual cells. Thus, final differentiated sub-populations could be used to deduce order, timing, and duration of transient chemical events. +
Engineered consortia are a major research focus for synthetic biologists because they can implement sophisticated behaviors inaccessible to single-strain systems. However, this functional capacity is constrained by their constituent strains’ ability to engage in complex communication. DNA messaging, by enabling information-rich channel-decoupled communication, is a promising candidate architecture for implementing complex communication. But its major advantage, its messages’ dynamic mutability, is still unexplored. We develop a framework for addressable and adaptable DNA messaging that leverages all three of these advantages and implement it using plasmid conjugation in E. coli. Our system can bias the transfer of messages to targeted receiver strains by 100- to 1000-fold, and their recipient lists can be dynamically updated in situ to control the flow of information through the population. This work lays the foundation for future developments that further utilize the unique advantages of DNA messaging to engineer previously-inaccessible levels of complexity into biological systems. +
R
Engineering microbial consortia is an important new frontier for synthetic biology given its efficiency in performing complex tasks and endurance to environmental uncertainty. Most synthetic circuits regulate population level behaviors via cell-to-cell communications, which are affected by spatially heterogeneous environments. Therefore, it is important to understand the limits on controlling system dynamics that are determined by interconnections among cell agents and provide a control strategy for engineering consortia. Here, we build a network model for a fractional population control circuit in two-strain consortia, and characterize the cell-to-cell communication network by topological properties, such as symmetry, locality and connectivity. Using linear network control theory, we relate the network topology to system output tracking performance. We analytically and numerically demonstrate that the minimum network control energy for accurate tracking depends on locality difference between two cell populations and how strongly the controller node contributes to communication strength. To realize robust consortia, we can manipulate the communication network topology and construct strongly connected consortia by altering chemicals in environments. Our results ground the expected cell population dynamics in its spatially organized communication network, and inspire directions in cooperative control in microbial consortia. +
I
Ensuring safety through set invariance has proven a useful method in a variety of applications in robotics and control. However, finding analytical expressions for maximal invariant sets, so as to maximize the operational freedom of the system without compromising safety, is notoriously difficult for high-dimensional systems with input constraints. Here we present a generic method for characterizing invariant sets of nth-order integrator systems, based on analyzing roots of univariate polynomials. Additionally, we obtain analytical expressions for the orders n <= 4. Using differential flatness we subsequently leverage the results for the n = 4 case to the problem of obstacle avoidance for quadrotor UAVs. The resulting controller has a light computational footprint that showcases the power of finding analytical expressions for control-invariant sets. +
C
Construction of an inducible amyloid expression circuit in Bacillus megaterium: A case study with CsgA and TasA +
Environmental applications of synthetic biology such as water remediation require engineered strains to function robustly in a fluctuating and potentially hostile environment. The construction of synthetic biofilm formation circuits could potentially alleviate this issue by promoting cell survival. Towards this end, we construct a xylose-inducible system for the expression of the functional amyloids CsgA and TasA in the soil bacterium Bacillus megaterium. We find that although both amyloids are expressed, only TasA is successfully exported from the cells. Furthermore, expression of CsgA results in a significant growth penalty for the cells while expression of TasA does not. Finally, we show that TasA expression conveys a small but detectable increase in cells’ adhesion to nickel beads. These results suggest that TasA is a promising candidate for future work on synthetic biofilm formation in B. megaterium. +
P
Establishing performance metrics is a key part of a systematic design process. In particular, specifying metrics useful for quantifying performance in the ongoing efforts towards biomolecular circuit design is an important problem. Here we address this issue for the design of a fast biomolec- ular step response that is uniform across different cells and widely different environmental conditions using a combination of simple mathematical models and experimental measure- ments using single-cell time-lapse microscopy. We evaluate two metrics, the difference of the step response from an ideal step and the relative difference between multiple realizations of the step response, that can provide a single number to measure performance. We use a model of protein production- degradation to show that these performance metrics correlate with response features of speed and noise. Finally, we work through an experimental methodology to estimate these metrics for step responses that have been acquired for inducible protein expression circuits in E. coli. These metrics will be useful to evaluate biomolecular step responses, as well as for setting similar performance measures for other design goals. +
