Sepsis, a manifestation of the body's inflammatory response to injury and infection, has a mortality rate of between 28%-50% and affects approximately 1 million patients annually in the United States. Currently, there are no therapies targeting the cellular/molecular processes driving sepsis that have demonstrated the ability to control this disease process in the clinical setting. We propose that this is in great part due to the considerable heterogeneity of the clinical trajectories that constitute clinical "sepsis," and that determining how this system can be controlled back into a state of health requires the application of concepts drawn from the field of dynamical systems. In this work, we consider the human immune system to be a random dynamical system, and investigate its potential controllability using an agent-based model of the innate immune response (the Innate Immune Response ABM or IIRABM) as a surrogate, proxy system. Simulation experiments with the IIRABM provide an explanation as to why single/limited cytokine perturbations at a single, or small number of, time points is unlikely to significantly improve the mortality rate of sepsis. We then use genetic algorithms (GA) to explore and characterize multi-targeted control strategies for the random dynamical immune system that guide it from a persistent, non-recovering inflammatory state (functionally equivalent to the clinical states of systemic inflammatory response syndrome (SIRS) or sepsis) to a state of health. We train the GA on a single parameter set with multiple stochastic replicates, and show that while the calculated results show good generalizability, more advanced strategies are needed to achieve the goal of adaptive personalized medicine. This work evaluating the extent of interventions needed to control a simplified surrogate model of sepsis provides insight into the scope of the clinical challenge, and can serve as a guide on the path towards true "precision control" of sepsis. Sepsis, characterized by the body's inflammatory response to injury and infection, has a mortality rate of between 28%-50% and affects approximately 1 million patients annually in the United States. Currently, there are no therapies targeting the cellular/molecular processes driving sepsis that have demonstrated the ability to control this disease process. In this work, we utilize a computational model of the human immune response to infectious injury to offer an explanation as to why previously attempted treatment strategies are inadequate and why the current approach to drug/therapy-development is inadequate. We then use evolutionary computation algorithms to explore drug-intervention space using this same computational model. This allows us to characterize the scale and scope of interventions needed to successfully control sepsis, as well as the types of data needed to derive these interventions. We demonstrate that multi-point and time-dependent varying controls are necessary and able to control the cytokine network dynamics of the immune system. 2 28%-50%, each year [1]. Attempts to discover biologically-targeted therapies for sepsis have thus far been focused 3 on manipulating a single mediator/cytokine, generally administered with either a single dose or over a very short 4 course (<72 hours) [2, 3] . Unfortunately, all these attempts, have been unsuccessful [2, 3] , likely due to both the 5 nonlinear nature of the human inflammatory signaling network and the paucity of clinical time-course data to 6 place network relationships in context. In fact, we would propose that the universal failure to find effective 7 cellular/molecular control strategies effective at the clinical level raises the question as to whether the system can 8 be effectively controlled at all. The rationale for the current investigation is an attempt to address this 9 fundamental question: can the trajectory of clinical sepsis be controlled, and if so, what is the scale and scope of 10 the therapeutic interventions required to do so? It is well known in biology that the systemic response to identical perturbations in genetically identical 12 individuals (i.e., mice) is governed according to some probability distribution. In a chaotic system, this small 13 stochastic variability in response can ultimately lead to a radically different final state [4] . It logically follows 14 then that a single time point/single cytokine intervention is unlikely to be successful on a broad range of patients 15 with a broad range of conditions that have led to the state of sepsis. The challenge, however, is that the range of 16 possible interventions, which is a function of the number of potential molecular targets, the extent to which they 17 are modified, the time at which such modification can occur and the combinations thereof, is staggering, and 18 cannot be tractably investigated given the logistical and practical limitations of both experimental and clinical 19 research. We propose to address this challenge by the use of evolutionary computing (in the form of genetic 20 algorithms) applied to a sufficiently complex, albeit abstracted, proxy computational model of sepsis. We have previously proposed that dynamic computational modeling, and specifically agent based modeling, can be used to represent mechanistic biological knowledge in a way that reproduces the non-linear dynamics of 23 the real world system [5, 6] . ABM's have been used to study and model a wide variety of biological systems [7], 24 from general purpose anatomic/cell-for-cell representations of organ systems capable of reproducing multiple 25 independent phenomena [6] to platforms for drug development [8] , and are frequently used to model non-linear 26 dynamical systems such as the human immune system [9, 10] . Specifically, was have previously developed an 27
Also similar in goal but different in methodology is a recent approach using genetic algorithms to search for a non-adaptive control strategy using the same IIRABM as this work REF .
Determining controllability of sepsis using genetic algorithms on a proxy agent-based model of systemic inflammation
bioRxiv
Biology