Addressing current challenges in cancer immunotherapy with mathematical and computational modeling

The goal of cancer immunotherapy is to boost a patient's immune response to a tumor. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumor microenvironment, immune-modulating effects of conventional treatments, and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modeling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumor classification, optimal treatment scheduling, and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modelers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumor-immune biology. We conclude the review with recommendations for modelers both with respect to methodology and biological direction that might help keep modelers at the forefront of cancer immunotherapy development.Comment: Accepted for publication in the Journal of the Royal Society Interfac

An Immune Inspired Approach to Anomaly Detection

The immune system provides a rich metaphor for computer security: anomaly detection that works in nature should work for machines. However, early artificial immune system approaches for computer security had only limited success. Arguably, this was due to these artificial systems being based on too simplistic a view of the immune system. We present here a second generation artificial immune system for process anomaly detection. It improves on earlier systems by having different artificial cell types that process information. Following detailed information about how to build such second generation systems, we find that communication between cells types is key to performance. Through realistic testing and validation we show that second generation artificial immune systems are capable of anomaly detection beyond generic system policies. The paper concludes with a discussion and outline of the next steps in this exciting area of computer security.Comment: 19 pages, 4 tables, 2 figures, Handbook of Research on Information Security and Assuranc

Molecular modelling of dendrimers for nanoscale applications

Dendrimers are well defined, highly branched macromolecules that radiate from a central core and are synthesized through a stepwise, repetitive reaction sequence that guarantees complete shells for each generation, leading to polymers that are monodisperse. The synthetic procedures developed for dendrimer preparation permit nearly complete control over the critical molecular design parameters, such as size, shape, surface/interior chemistry, flexibility, and topology. Recent results suggest that dendritic polymers may provide the key to developing a reliable and economical fabrication and manufacturing route to functional nanoscale materials that would have unique properties (electronic, optical, opto-electronic, magnetic, chemical, or biological). In turn, these could be used in designing new nanoscale devices. In this paper, we determine the 3D molecular structure of various dendrimers with continuous configurational Boltzmann biased direct Monte Carlo method and study their energetic and structural properties using molecular dynamics after annealing these molecular representations

From secretome analysis to immunology: chitosan induces major alterations in the activation of dendritic cells via a TLR4-dependent mechanism

Dendritic cells are known to be activated by a wide range of microbial products, leading to cytokine production and increased levels of membrane markers such as major histocompatibility complex class II molecules. Such activated dendritic cells possess the capacity to activate na\"ive T cells. In the present study we demonstrated that immature dendritic cells secrete both the YM1 lectin and lipocalin-2. By testing the ligands of these two proteins, chitosan and siderophores, respectively, we also demonstrated that chitosan, a degradation product of various fungal and protozoal cell walls, induces an activation of dendritic cells at the membrane level, as shown by the up-regulation of membrane proteins such as class II molecules, CD80 and CD86 via a TLR4-dependent mechanism, but is not able to induce cytokine production. This led to the production of activated dendritic cells unable to stimulate T cells. However, costimulation with other microbial products overcame this partial activation and restored the capacity of these activated dendritic cells to stimulate T cells. In addition, successive stimulation with chitosan and then by lipopolysaccharide induced a dose-dependent change in the cytokinic IL-12/IL-10 balance produced by the dendritic cells

Modeling Brain Circuitry over a Wide Range of Scales

If we are ever to unravel the mysteries of brain function at its most fundamental level, we will need a precise understanding of how its component neurons connect to each other. Electron Microscopes (EM) can now provide the nanometer resolution that is needed to image synapses, and therefore connections, while Light Microscopes (LM) see at the micrometer resolution required to model the 3D structure of the dendritic network. Since both the topology and the connection strength are integral parts of the brain's wiring diagram, being able to combine these two modalities is critically important. In fact, these microscopes now routinely produce high-resolution imagery in such large quantities that the bottleneck becomes automated processing and interpretation, which is needed for such data to be exploited to its full potential. In this paper, we briefly review the Computer Vision techniques we have developed at EPFL to address this need. They include delineating dendritic arbors from LM imagery, segmenting organelles from EM, and combining the two into a consistent representation

Neuro-memristive Circuits for Edge Computing: A review

The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of neuro-memristive circuits for edge computing