Amor A. Menezes

Towards synthetic biological approaches to resource utilization on space missions.

This paper demonstrates the significant utility of deploying non-traditional biological techniques to harness available volatiles and waste resources on manned missions to explore the Moon and Mars. Compared with anticipated non-biological approaches, it is determined that for 916 day Martian missions: 205 days of high-quality methane and oxygen Mars bioproduction with Methanobacterium thermoautotrophicum can reduce the mass of a Martian fuel-manufacture plant by 56%; 496 days of biomass generation with Arthrospira platensis and Arthrospira maxima on Mars can decrease the shipped wet-food mixed-menu mass for a Mars stay and a one-way voyage by 38%; 202 days of Mars polyhydroxybutyrate synthesis with Cupriavidus necator can lower the shipped mass to three-dimensional print a 120 m3 six-person habitat by 85% and a few days of acetaminophen production with engineered Synechocystis sp. PCC 6803 can completely replenish expired or irradiated stocks of the pharmaceutical, thereby providing independence from unmanned resupply spacecraft that take up to 210 days to arrive. Analogous outcomes are included for lunar missions. Because of the benign assumptions involved, the results provide a glimpse of the intriguing potential of ‘space synthetic biology’, and help focus related efforts for immediate, near-term impact.

Grand challenges in space synthetic biology

Space synthetic biology is a branch of biotechnology dedicated to engineering biological systems for space exploration, industry and science. There is significant public and private interest in designing robust and reliable organisms that can assist on long-duration astronaut missions. Recent work has also demonstrated that such synthetic biology is a feasible payload minimization and life support approach as well. This article identifies the challenges and opportunities that lie ahead in the field of space synthetic biology, while highlighting relevant progress. It also outlines anticipated broader benefits from this field, because space engineering advances will drive technological innovation on Earth.

A Combined Seed-Identification and Generation Analysis Algorithm for Self-Reproducing Systems

This paper is motivated by the need to minimize the payload mass required to establish an extraterrestrial robotic colony. The basic premise is that the colony will consist of individual robots that have the capability to self-reproduce. In this paper, self-reproduction is achieved by the actions of a robot on available resources. Hence, a seed for the colony consists of a set of robots and a set of resources. The technical problem addressed is the identification of a seed for a class of generation systems. An algorithm is provided for the solution of this problem, and is illustrated on a self-replicating system that has been documented in the literature.

Targeted clinical control of trauma patient coagulation through a thrombin dynamics model

A control-oriented dynamical system model of trauma coagulation helps tailor the resuscitation of severely injured patients. The key to resuscitation is in the blood In the setting of severe trauma, some patients develop acute traumatic coagulopathy, impaired coagulation that can occur in response to shock. For patients who are already bleeding and then develop coagulopathy, there is no time to carefully perform laboratory analysis, and blood products are usually transfused according to standardized protocols. Because these protocols are not tailored to individual patients or injuries, this can result in insufficient or excessive blood product transfusions, which contribute to the high risk of mortality. Using dynamic modeling, Menezes et al. demonstrated a method for calculating each patient’s transfusion requirements using only laboratory values that can be easily and quickly obtained in the emergency setting, which should allow for individually tailored resuscitation. We present a methodology for personalizing the clinical treatment of severely injured patients with acute traumatic coagulopathy (ATC), an endogenous biological response of impaired coagulation that occurs early after trauma and shock and that is associated with increased bleeding, morbidity, and mortality. Despite biological characterization of ATC, it is not easily or rapidly diagnosed, not always captured by slow laboratory testing, and not accurately represented by coagulation models. This lack of knowledge, combined with the inherent time pressures of trauma treatment, forces surgeons to treat ATC patients according to empirical resuscitation protocols. These entail transfusing large volumes of poorly characterized, nontargeted blood products that are not tailored to an individual, the injury, or coagulation dynamics. Massive transfusion mortality remains at 40 to 70% in the best of trauma centers. As an alternative to blunt treatments, time-consuming tests, and mechanistic models, we used dynamical systems theory to create a simple, biologically meaningful, and highly accurate model that (i) quickly forecasts a driver of downstream coagulation, thrombin concentration after tissue factor stimulation, using rapidly measurable concentrations of blood protein factors and (ii) determines the amounts of additional coagulation factors needed to rectify the predicted thrombin dynamics and potentially remedy ATC. We successfully demonstrate in vitro thrombin control consistent with the model. Compared to another model, we decreased the mean errors in two key trauma patient parameters: peak thrombin concentration after tissue factor stimulation and the time until this peak occurs. Our methodology helps to advance individualized resuscitation of trauma-induced coagulation deficits.

Optimal search efficiency of Barker’s algorithm with an exponential fitness function

Markov Chain Monte Carlo (MCMC) methods may be employed to search for a probability distribution over a bounded space of function arguments to estimate which argument(s) optimize(s) an objective function. This search-based optimization requires sampling the suitability, or fitness, of arguments in the search space. When the objective function or the fitness of arguments vary with time, significant exploration of the search space is required. Search efficiency then becomes a more relevant measure of the usefulness of an MCMC method than traditional measures such as convergence speed to the stationary distribution and asymptotic variance of stationary distribution estimates. Search efficiency refers to how quickly prior information about the search space is traded-off for search effort savings. Optimal search efficiency occurs when the entropy of the probability distribution over the space during search is maximized. Whereas the Metropolis case of the Hastings MCMC algorithm with fixed candidate generation is optimal with respect to asymptotic variance of stationary distribution estimates, this paper proves that Barker’s case is optimal with respect to search efficiency if the fitness of the arguments in the search space is characterized by an exponential function. The latter instance of optimality is beneficial for time-varying optimization that is also model-independent.

A stochastic drift counteraction optimal control approach to glider flight management

This paper formulates two stochastic optimal control problems to determine optimal glider flight management decisions that include a time-varying selection of glider ground speed and an amount of time to spend climbing in a randomly encountered thermal. In the first problem, the objective is to maximize the expected glider range while maintaining glider altitude within given limits. In the second problem, the objective is to maximize the expected range in which the glider is able to follow a moving ground vehicle within a prescribed distance while maintaining glider altitude within given limits. Both problems are treated using stochastic drift counteraction optimal control. Simulation results are reported and discussed. The work has application to glider flight performance improvement, and to noiseless surveillance by unmanned air vehicles.

Modeling and control of cyclic systems in xerography

This paper is devoted to the scientific study and engineering application of cyclic systems. Cyclic systems are non-traditional plants, containing devices with rotating dynamics along with actuators and sensors fixed in inertial space. The combination of rotating dynamics and inertially-fixed inputs and outputs leads to one-per-revolution (or stroboscopic) actuation and sensing. Control of cyclic systems amounts to designing a regulator that uses stroboscopic actuation and sensing to force the system into the desired regime. Although cyclic systems are periodic, the general theory of periodic control is not immediately applicable due to stroboscopic actuation and sensing. Because of rotating dynamics, the theory of impulsive control is not applicable as well. This work develops an approach to the control of systems with both rotating dynamics and stroboscopic instrumentation, and reports the initial application to a xerographic process.

Stable hierarchical model predictive control using an inner loop reference model and λ -contractive terminal constraint sets

This paper proposes a novel hierarchical model predictive control (MPC) strategy that guarantees overall system stability. This method differs significantly from previous approaches to guaranteeing overall stability, which have relied upon a multi-rate framework where the inner loop (low level) is updated at a faster rate than the outer loop (high level), and the inner loop must reach a steady state within each outer loop time step. In contrast, the method proposed in this paper is aimed at stabilizing the origin of an error system characterized by the difference between the inner loop state and the state specified by a full-order reference model. This makes the method applicable to systems with reduced levels of time scale separation. This paper proposes a framework for guaranteeing stability that leverages the use of the reference model, in conjunction with λ -contractive constraint sets for both the inner and outer loops. The effectiveness of the proposed reference model-based strategy is shown through simulation on an existing stirred-tank reactor problem, where we demonstrate that the MPC optimization problem remains feasible and that the system remains stable and continues to perform well when time scale separation between the inner and outer loops is reduced.

Cyclic Control: Reference Tracking and Disturbance Rejection

Cyclic control (CC) is concerned with systems comprised of multiple plants controlled by a single actuator and sensor. Such systems arise naturally in rotating machinery with actuators and sensors fixed in inertial space. This paper investigates the problem of reference tracking and disturbance rejection in CC systems and illustrates the results through the control of the fusing stage of a xerographic process.

Efficient search and responsiveness trade-offs in a Markov chain model of evolution in dynamic environments.

Motivated by the desire to study evolutionary responsiveness in fluctuating environments, and by the current interest in analyses of evolution that merge notions of fitness maximization with dynamical systems concepts such as Lyapunov functions, this paper models natural evolution with a simple stochastic dynamical system that can be represented as a Markov chain. The process maximizes fitness globally via search and has links to information and entropy. These links suggest that a possible rationale for evolution with the exponential fitness functions observed in nature is that of optimally-efficient search in a dynamic environment, which represents the quickest trade-off of prior information about the genotype search space for search effort savings after an environment perturbation. A Lyapunov function is also provided that relates the stochastic dynamical system model with search information, and the model shows that evolution is not gradient-based but dwells longer on more fit outcomes. The model further indicates that tuning the amount of selection trades off environment responsiveness with the time to reach fit outcomes, and that excessive selection causes a loss of responsiveness, a result that is validated by the literature and impacts efforts in directed evolution.