Multiple sclerosis (MS) is a neurodegenerative disease in which misdirected, persistent activity of the immune system degrades the protective myelin sheaths of nerve axons. Historically, treatment of MS has relied on disease-modifying therapies that involve immunosuppression, such as targeting of the blood-brain barrier (BBB) to restrict lymphocyte movement. New therapeutic ideas in the development pipeline are instead designed to promote populations of myelin producing cells, oligodendrocytes, by exploiting their innate resilience to the stressors of MS or restoring their numbers. Given the significant advancements made in immunological disease understanding due to mathematical and computational modelling, we sought to develop a platform to (1) interrogate our understanding of the neuroimmunological mechanisms driving MS development and (2) examine the impact of different therapeutic strategies. To this end we propose a novel, open-source, agent-based model of lesion development in the CNS. Our model includes crucial populations of T cells, perivascular macrophages, and oligodendrocytes. We examine the sensitivity of the model to key parameters related to disease targets and conclude that lesion stabilisation can be achieved when targeting the integrated stress response of oligodendrocytes. Most significantly, complete prevention of lesion formation is observed when a combination of approved BBB-permeability targeting therapies and integrated-stress response targeting therapies is administered, suggesting the potential to strike a balance between a patient's immune inflammation and their reparative capacity. Given that there are many open questions surrounding the etiology and treatment of MS, we hope that this malleable platform serves as a tool for experimentalists and modellers to test and generate further hypotheses regarding this disease.

Robust Perfect Adaptation (RPA), or robust homeostasis, is a ubiquitous property of biological systems, and ensures that key properties of the system can reset themselves in response to environmental disturbances, and maintain a fixed value (the system's 'setpoint') at steady-state.RPA is a structural property of biological networks, and is independent of special parameter choices-hence, 'robust'.Simple network configurations that can support RPA have been known for several decades [1-3], but until recently all known RPA-permissive network designs were limited in size and complexity to fewer than five interacting molecules.By contrast, almost all molecular networks of functional importance to living organisms comprise far more than five molecules, particularly in the context of cellular signal transduction and cellular metabolism, where hundreds or even thousands of distinct proteins or phospho-forms can dynamically assemble into vast and intricate networks of molecular interactions.How could the extraordinarily complex networks that have self-organized and diversified throughout evolution be compatible with such robust and sophisticated functions as RPA?Over the past several years, we have provided definitive answers to this question [4-6]both at level of the network macroscale [4], in terms of a complete and exhaustive set of network design principles or 'topologies' that are compatible with RPA, and in terms of the intricate intermolecular interactions that make the requisite 'computations' at the microscale of biochemical reaction networks [7].As a consequence of these discoveries, RPA now stands alone as a fundamental biological response for which there exists a universal explanatory framework at the molecular level and an exhaustive set of network design principles 7 , encompassing single-input/single output-networks subjected to constant-in-time disturbances, under the assumption of stability. PLOS Computational Biology recently published an article by Bhattacharya et al.[8] which claims to 'discover' RPA-capable biological network structures using control theoretic approaches.Since we ourselves had previously published a complete solution space for all possible RPA-capable network architectures, we would like to provide some additional clarification on this new article [8] in the context of our more general and comprehensive prior study [4].We also seek to provide an expanded view of the many exciting developments in this field, and the grand challenges for the future.First and foremost, we acknowledge that the Bhattacharya et al. article [8] develops a systematic control-theoretic approach, along with an analysis of stability, which is able to identify a variety of RPA-capable network structures in addition to requirements for non-zero sensitivity in these networks.The authors are also able to show that RPA minimizes the time taken for

The prognosis for pancreatic ductal adenocarcinoma (PDAC) patients has not significantly improved in the past 3 decades, highlighting the need for more effective treatment approaches. Poor patient outcomes and lack of response to therapy can be attributed, in part, to a lack of uptake of perfusion of systemically administered chemotherapeutic drugs into the tumour. Wet-spun alginate fibres loaded with the chemotherapeutic agent gemcitabine have been developed as a potential tool for overcoming the barriers in delivery of systemically administrated drugs to the PDAC tumour microenvironment by delivering high concentrations of drug to the tumour directly over an extended period. While exciting, the practicality, safety, and effectiveness of these devices in a clinical setting requires further investigation. Furthermore, an in-depth assessment of the drug-release rate from these devices needs to be undertaken to determine whether an optimal release profile exists. Using a hybrid computational model (agent-based model and partial differential equation system), we developed a simulation of pancreatic tumour growth and response to treatment with gemcitabine loaded alginate fibres. The model was calibrated using in vitro and in vivo data and simulated using a finite volume method discretisation. We then used the model to compare different intratumoural implantation protocols and gemcitabine-release rates. In our model, the primary driver of pancreatic tumour growth was the rate of tumour cell division. We were able to demonstrate that intratumoural placement of gemcitabine loaded fibres was more effective than peritumoural placement. Additionally, we quantified the efficacy of different release profiles from the implanted fibres that have not yet been tested experimentally. Altogether, the model developed here is a tool that can be used to investigate other drug delivery devices to improve the arsenal of treatments available for PDAC and other difficult-to-treat cancers in the future.

Innovation in synthetic biology often still depends on large-scale experimental trial and error, domain expertise, and ingenuity. The application of rational design engineering methods promises to make this more efficient, faster, cheaper, and safer. However, this requires mathematical models of cellular systems. For these models, we then have to determine if they can meet our intended target behavior. Here, we develop two complementary approaches that allow us to determine whether a given molecular circuit, represented by a mathematical model, is capable of fulfilling our design objectives. We discuss algebraic methods that are capable of identifying general principles guaranteeing desired behavior; we provide an overview of Bayesian design approaches that allow us to choose a model that has the highest probability of fulfilling our design objectives from a set of models. We discuss their uses in the context of biochemical adaptation and, then, consider how robustness can and should affect our design approach.