Tackling the challenges of interdisciplinary bioscience

The ultimate goal for biology is to become a science that formulates our understanding of subcellular, cellular and multicellular systems in terms of quantitative, holistic models that are underpinned by the rigorous principles of the physical sciences and mathematics. This can only be achieved through interdisciplinary research that draws heavily on the expertise and technologies of the physical sciences, engineering, computation and mathematics. Here, I discuss the benefits and challenges (both intellectual and practical) of interdisciplinary bioscience.     

The Evolution of Human Brain Development

The human brain is a large and complex organ, setting us apart from other primates. It allows us to exhibit highly sophisticated cognitive and behavioral abilities. Therefore, our brain??s size and morphology are defining features of our species and our fossil ancestors and relatives. Endocasts, i.e., internal casts of the bony braincase, provide evidence about brain size and morphology in fossils. Based on endocasts, we know that our ancestors?? brains increased overall in size and underwent several reorganizational changes. However, it is difficult to relate evolutionary changes of size and shape of endocasts to evolutionary changes of cognition and behavior. We argue here that an understanding of the tempo and mode of brain development can help to interpret the evolution of our brain and the associated cognitive and behavioral changes. To do so, we review structural brain development, cognitive development, and ontogenetic changes of endocranial size and shape in living individuals on the one hand, and ontogenetic patterns (size increase and shape change) in fossil hominins and their evolutionary change on the other hand. Tightly integrating our knowledge on these different levels will be the key of future work on the evolution of human brain development.     

The challenge of challenge: Can problem solving opportunities enhance animal welfare?

Cognitive mechanisms are an important part of the organization of the behavior systems of animals. In the wild, animals regularly face problems that they must overcome in order to survive and thrive. Solving such problems often requires animals to process, store, retrieve, and act upon information from the environment—in other words, to use their cognitive skills. For example, animals may have to use navigational, tool-making or cooperative social skills in order to procure their food. However, many enrichment programs for captive animals do not include the integration of these types of cognitive challenges. Thus, foraging enrichments typically are designed to facilitate the physical expression of feeding behaviors such as food-searching and food consumption, but not to facilitate complex problem solving behaviors related to food acquisition. Challenging animals by presenting them with problems is almost certainly a source of frustration and stress. However, we suggest here that this is an important, and even necessary, feature of an enrichment program, as long as animals also possess the skills and resources to effectively solve the problems with which they are presented. We discuss this with reference to theories about the emotional consequences of coping with challenge, the association between lack of challenge and the development of abnormal behavior, and the benefits of stress (arousal) in facilitating learning and memory of relevant skills. Much remains to be done to provide empirical support for these theories. However, they do point the way to a practical approach to improving animal welfare—to design enrichments to facilitate the cognitive mechanisms which underlie the performance of complex behaviors that cannot be performed due to the restrictions inherent to the captive environment.     

Functional genomic technologies: creating new paradigms for fundamental and applied biology.

New technologies that analyze the behavior of thousands of genes in parallel are creating, for the first time, a foundation of data for building integrated models of cellular processes. This review discusses the general issues of utilizing genomic methods in fundamental and applied research settings, using the study of stress responses and improvement of secondary metabolite production as examples. A fusion of concepts from biological and nonbiological disciplines, including mathematics, computer science, physics, chemistry, and engineering, is required to address the theoretical and experimental challenges facing the field of genomics, and together promise great breakthroughs in our understanding and engineering of cellular systems.     

Cover Image,Volume 116, Number 9, September 2019

The new and rapid advancement in the complexity of biologics drug discovery has been driven by a deeper understanding of biological systems combined with innovative new therapeutic modalities, paving the way to breakthrough therapies for previously intractable diseases. These exciting times in biomedical innovation require the development of novel technologies to facilitate the sophisticated, multifaceted, high-paced workflows necessary to support modern large molecule drug discovery. A high-level aspiration is a true integration of “lab-on-a-chip” methods that vastly miniaturize cellulmical experiments could transform the speed, cost, and success of multiple workstreams in biologics development. Several microscale bioprocess technologies have been established that incrementally address these needs, yet each is inflexibly designed for a very specific process thus limiting an integrated holistic application. A more fully integrated nanoscale approach that incorporates manipulation, culture, analytics, and traceable digital record keeping of thousands of single cells in a relevant nanoenvironment would be a transformative technology capable of keeping pace with today's rapid and complex drug discovery demands. The recent advent of optical manipulation of cells using light-induced electrokinetics with micro- and nanoscale cell culture is poised to revolutionize both fundamental and applied biological research. In this review, we summarize the current state of the art for optical manipulation techniques and discuss emerging biological applications of this technology. In particular, we focus on promising prospects for drug discovery workflows, including antibody discovery, bioassay development, antibody engineering, and cell line development, which are enabled by the automation and industrialization of an integrated optoelectronic single-cell manipulation and culture platform. Continued development of such platforms will be well positioned to overcome many of the challenges currently associated with fragmented, low-throughput bioprocess workflows in biopharma and life science research.     

Cerebral cartography and connectomics

Cerebral cartography and connectomics pursue similar goals in attempting to create maps that can inform our understanding of the structural and functional organization of the cortex. Connectome maps explicitly aim at representing the brain as a complex network, a collection of nodes and their interconnecting edges. This article reflects on some of the challenges that currently arise in the intersection of cerebral cartography and connectomics. Principal challenges concern the temporal dynamics of functional brain connectivity, the definition of areal parcellations and their hierarchical organization into large-scale networks, the extension of whole-brain connectivity to cellular-scale networks, and the mapping of structure/function relations in empirical recordings and computational models. Successfully addressing these challenges will require extensions of methods and tools from network science to the mapping and analysis of human brain connectivity data. The emerging view that the brain is more than a collection of areas, but is fundamentally operating as a complex networked system, will continue to drive the creation of ever more detailed and multi-modal network maps as tools for on-going exploration and discovery in human connectomics.     

Multiscale agent-based cancer modeling

Agent-based modeling (ABM) is an in silico technique that is being used in a variety of research areas such as in social sciences, economics and increasingly in biomedicine as an interdisciplinary tool to study the dynamics of complex systems. Here, we describe its applicability to integrative tumor biology research by introducing a multi-scale tumor modeling platform that understands brain cancer as a complex dynamic biosystem. We summarize significant findings of this work, and discuss both challenges and future directions for ABM in the field of cancer research.     

Manipulating neural activity in physiologically classified neurons: triumphs and challenges

Understanding brain function requires knowing both how neural activity encodes information and how this activity generates appropriate responses. Electrophysiological, imaging and immediate early gene immunostaining studies have been instrumental in identifying and characterizing neurons that respond to different sensory stimuli, events and motor actions. Here we highlight approaches that have manipulated the activity of physiologically classified neurons to determine their role in the generation of behavioural responses. Previous experiments have often exploited the functional architecture observed in many cortical areas, where clusters of neurons share response properties. However, many brain structures do not exhibit such functional architecture. Instead, neurons with different response properties are anatomically intermingled. Emerging genetic approaches have enabled the identification and manipulation of neurons that respond to specific stimuli despite the lack of discernable anatomical organization. These approaches have advanced understanding of the circuits mediating sensory perception, learning and memory, and the generation of behavioural responses by providing causal evidence linking neural response properties to appropriate behavioural output. However, significant challenges remain for understanding cognitive processes that are probably mediated by neurons with more complex physiological response properties. Currently available strategies may prove inadequate for determining how activity in these neurons is causally related to cognitive behaviour.     

Towards high-yield production of pharmaceutical proteins with plant cell suspension cultures

“Molecular farming” in plants with significant advantages in cost and safety is touted as a promising platform for the production of complex pharmaceutical proteins. While whole-plant produced biopharmaceuticals account for a significant portion of the preclinical and clinical pipeline, plant cell suspension culture, which integrates the merits of whole-plant systems with those of microbial fermentation, is emerging as a more compliant alternative “factory”. However, low protein productivity remains a major obstacle that limits extensive commercialization of plant cell bioproduction platform. This review highlights the advantages and recent progress in plant cell culture technology and outlines viable strategies at both the biological and process engineering levels for advancing the economic feasibility of plant cell-based protein production. Approaches to overcome and solve the associated challenges of this culture system that include non-mammalian glycosylation and genetic instability will also be discussed.     

神经工程与脑-机接口

神经工程是近年来在生物医学工程领域备受关注的学科发展新方向。它运用神经科学和工程学的方法来分析神经功能并为神经功能缺失与紊乱的修复提供新的解决问题的方案;而脑-机接口则是当前神经工程领域中最活跃的研究方向之一。脑-机接口是在脑与计算机或其他外部设备之间建立的直接的通信和交流通道。在脑-机接口系统中,具有特定模式的脑信号携带着受试者希望表达的意愿,计算机将接收到的脑信号转换成相应的控制命令,于是那些有运动障碍的残疾人就可以利用脑-机接口系统来实现与外界的交流与对外部设备的控制。在基于脑电信号的脑-机接口系统中,受试者产生的脑信号大致可以分为内源性（endogenous）和外源性（exogenous）两类。其中外源性的成分主要取决于外部物理刺激（视觉、听觉或触觉）的参数而与认知行为无关;而内源性成分则主要由认知行为产生而与外部的物理刺激无关。在许多情况下,脑－机接口中的瞬态诱发电位通常都同时包含着内源性和外源性两种成分。寻找新的脑－机接口模式使之能显著提升记录脑电信号中的内源性与外源性成分在脑－机接口研究中具有重要意义。本文中将介绍一种基于运动起始时刻（motion—onset）的新的脑-机接口实验范式。本文的最后还探讨了脑－机接口未来发展的趋势与展望。     

生物实验信息管理模块建模技术研究

实验信息管理模块是生物实验室信息管理系统的核心,其设计的好坏直接关系到整个系统建设的成败。针对生物实验室实验流程灵活,数据流网络复杂等特点,研究设计了一套实验信息管理模块的模型,并将研究的模型用于基因测序实验室信息管理系统的开发并取得了成功。实践证明,该模型为实验室信息系统(LIMS)中实验管理模块的开发提供了理论支撑,并且提高了LIMS的开发效率。     

Emotional and cognitive changes during adolescence

Adolescence is a critical period for maturation of neurobiological processes that underlie higher cognitive functions and social and emotional behavior. Recent studies have applied new advances in magnetic resonance imaging to increase understanding of the neurobiological changes that occur during the transition from childhood to early adulthood. Structural imaging data indicate progressive and regressive changes in the relative volumes of specific brain regions, although total brain volume is not significantly altered. The prefrontal cortex matures later than other regions and its development is paralleled by increased abilities in abstract reasoning, attentional shifting, response inhibition and processing speed. Changes in emotional capacity, including improvements in affective modulation and discrimination of emotional cues, are also seen during adolescence. Functional imaging studies using cognitive and affective challenges have shown that frontal cortical networks undergo developmental changes in processing. In summary, brain regions that underlie attention, reward evaluation, affective discrimination, response inhibition and goal-directed behavior undergo structural and functional re-organization throughout late childhood and early adulthood. Evidence from recent imaging studies supports a model by which the frontal cortex adopts an increasingly regulatory role. These neurobiological changes are believed to contribute, in part, to the range in cognitive and affective behavior seen during adolescence.     

Integrative database management for mouse development: systems and concepts

Cells in the developing embryo must integrate complex signals from the genome and environment to make decisions about their behavior or fate. The ability to understand the fundamental biology of the decision-making process, and how these decisions may go awry during abnormal development, requires a systems biology paradigm. Presently, the ability to build models with predictive capability in birth defects research is constrained by an incomplete understanding of the fundamental parameters underlying embryonic susceptibility, sensitivity, and vulnerability. Key developmental milestones must be parameterized in terms of system structure and dynamics, the relevant control methods, and the overall design logic of metabolic and regulatory networks. High-content data from genome-based studies provide some comprehensive coverage of these operational processes but a key research challenge is data integration. Analysis can be facilitated by data management resources and software to reveal the structure and function of bionetwork motifs potentially associated with an altered developmental phenotype. Borrowing from applied mathematics and artificial intelligence, we conceptualize a system that can help address the new challenges posed by the transformation of birth defects research into a data-driven science.     

Computational neuroscience and neuroinformatics: Recent progress and resources

The human brain and its temporal behavior correlated with development, structure, and function is a complex natural system even for its own kind. Coding and automation are necessary for modeling, analyzing and understanding the 86.1 ± 8.1 billion neurons, an almost equal number of non-neuronal glial cells, and the neuronal networks of the human brain comprising about 100 trillion connections. ‘Computational neuroscience’ which is heavily dependent on biology, physics, mathematics and computation addresses such problems while the archival, retrieval and merging of the huge amount of generated data in the form of clinical records, scientific literature, and specialized databases are carried out by ‘neuroinformatics’ approaches. Neuroinformatics is thus an interface between computer science and experimental neuroscience. This article provides an introduction to computational neuroscience and neuroinformatics fields along with their state-of-the-art tools, software, and resources. Furthermore, it describes a few innovative applications of these fields in predicting and detecting brain network organization, complex brain disorder diagnosis, large-scale 3D simulation of the brain, brain–computer, and brain-to-brain interfaces. It provides an integrated overview of the fields in a non-technical way, appropriate for broad general readership. Moreover, the article is an updated unified resource of the existing knowledge and sources for researchers stepping into these fields.     

Contributions and promise of human behavioral genetics

Human behavioral genetics has contributed greatly to our understanding of human behavioral development. Twin, family, and adoption studies have shown that genetic effects are ubiquitous and that both genes and environments contribute to individual differences in behavior. The unique ability of behavioral genetic methods to separate genetic from environmental effects has also led to important discoveries about how the environment works in development and to the elucidation of the complex ways environments and genes interact across the life span. Although quantitative methods have been the mainstay of the field of human behavioral genetics since Galton's time, the Human Genome Project and advances in molecular genetics are providing new tools and promise as we enter the 21st century. Thus the future of human behavioral genetics lies in the cross-disciplinary exchanges and collaborations that will increasingly occur in the years to come among quantitative and molecular scientists who work with both animal and human systems. This research may someday culminate in an understanding of the biological basis of behavior that spans from how the brain develops and functions to a grasp of how genes influence thought at the molecular level.     

Current state-of-the-art of auditory functional MRI (fMRI) on zebra finches: Technique and scientific achievements

Songbirds provide an excellent model system exhibiting vocal learning associated with an extreme brain plasticity linked to quantifiable behavioral changes. This animal model has thus far been intensively studied using electrophysiological, histological and molecular mapping techniques. However, these approaches do not provide a global view of the brain and/or do not allow repeated measures, which are necessary to establish correlations between alterations in neural substrate and behavior. In contrast, functional Magnetic Resonance Imaging (fMRI) is a non-invasive in vivo technique which allows one (i) to study brain function in the same subject over time, and (ii) to address the entire brain at once. During the last decades, fMRI has become one of the most popular neuroimaging techniques in cognitive neuroscience for the study of brain activity during various tasks ranging from simple sensory-motor to highly cognitive tasks. By alternating various stimulation periods with resting periods during scanning, resting and task-specific regional brain activity can be determined with this technique. Despite its obvious benefits, fMRI has, until now, only been sparsely used to study cognition in non-human species such as songbirds. The Bio-Imaging Lab (University of Antwerp, Belgium) was the first to implement Blood Oxygen Level Dependent (BOLD) fMRI in songbirds – and in particular zebra finches – for the visualization of sound perception and processing in auditory and song control brain regions. The present article provides an overview of the establishment and optimization of this technique in our laboratory and of the resulting scientific findings. The introduction of fMRI in songbirds has opened new research avenues that permit experimental analysis of complex sensorimotor and cognitive processes underlying vocal communication in this animal model.     

Peptoids at the 7th Summit: toward macromolecular systems engineering

Methods for facile synthesis of extraordinarily diverse peptide-like oligomers have placed peptoids at the center of a broad and vibrant area of foldamer science and technology. The 7th Peptoid Summit offered a perspective on the current state of peptoid science and technology and on prospects for engineering supramolecular assemblies that rival the complexity of biomolecular systems. Methods for engineering biomolecular systems based on DNA and protein are advancing rapidly, building a technology platform for engineering increasingly large and complex self-assembled nanosystems. A comparative review of the physical basis for DNA, protein, and peptoid engineering indicates that the characteristics of peptoids suit them for a strong role in developing self-assembled nanosystems. Physical parallels between peptoids and proteins indicate that peptoid engineering, like protein engineering, will require specialized software to support design. Access to novel side-chain functionality will enable peptoid designers to exploit novel binding interactions, including many that have been discovered and exploited in crystal engineering, a field that has extensively explored the self-assembly of small organic molecules to form well-ordered structures. Developments in DNA, protein, and inorganic nanotechnologies are converging to provide a technology platform for the design and fabrication of complex, functional, atomically precise nanosystems. Peptoid-based foldamer technologies, can contribute to this convergence, expanding the scope of the emerging field of atomically precise macromolecular nanosystems.     

Measuring and modeling the motor system with machine learning

The utility of machine learning in understanding the motor system is promising a revolution in how to collect, measure, and analyze data. The field of movement science already elegantly incorporates theory and engineering principles to guide experimental work, and in this review we discuss the growing use of machine learning: from pose estimation, kinematic analyses, dimensionality reduction, and closed-loop feedback, to its use in understanding neural correlates and untangling sensorimotor systems. We also give our perspective on new avenues, where markerless motion capture combined with biomechanical modeling and neural networks could be a new platform for hypothesis-driven research.