The Future of Biomedical Science
Research Project
This text will discuss current trends and future directions in biomedical sciences. In an attempt
to provide a well-supported analysis of this topic I reviewed recent achievements and current
issues in some areas of biomedical sciences and extrapolated this information to predict the
future. As much as I tried to provide an objective and generalizable prediction of the future
trends in biomedical sciences, my analysis is somewhat subjective and limited mostly to my
area of expertise, which includes pharmacology, medicinal chemistry, experimental oncology,
nanoscience and nanotechnology.
Current biomedical sciences display specific trends that are likely to continue at least for some
time in the future. These trends include, among others,
(i) the use of methods that generate big data,
(ii) experimental methods for analyses of single cells in large cell populations,
(iii)
computational modeling of complex biological systems,
(iv) integration of the “omics” data, and
(v) advanced understanding of the structure and function of biologically relevant molecules and their role in health and disease.
Considering the progress recently achieved in the field of
nanotechnology, one can safely predict that nanotechnology will play an important future role
in sciences in general and in biomedical sciences in particular. Furthermore, certain trends in
contemporary sciences strongly suggest that boundaries between biomedical sciences,
delimiting one scientific discipline from another, will be less distinct and will possibly disappear
in time. Consequently, multiple fields of biomedical science, as known today, will eventually
converge into the limited number of highly multidisciplinary fields of biomedical science. A
dominant position in biomedical sciences will be assumed by translational health research that
crosses barriers between basic and clinical research and applies findings from basic biomedical
sciences to prevent, predict or cure disease.
Methods that Generate Big Data
Methods that generate big data in biomedical research can be divided into two broad classes:
multiplex assays and high-throughput (or ultra-high-throughput) assays. While multiplex assays
generate much information (datapoints) from one subject or specimen by simultaneous
measurements of many different analytical signals, high-throughput methods generate one or a few datapoints from many subjects (specimens) analyzed in parallel. Big data are characterized by 3V: volume (amount of data), variety (diversity of data types) and velocity (speed of datageneration vs analysis).
Multiplex and high-throughput methods are becoming an everyday reality in contemporary
sciences owing to the technological advances that brought miniaturization, automatization,
integration and the ability to conduct highly parallel experiments in platforms known as a “labon-a-chip”. These platforms allow performing multiplex assays with a few specimens or highthroughput assays with many specimens in small compact chips that contain serially connected
microfluidic units, each of which is dedicated to a specific laboratory operation, such as reagent
storage and release, homogenization, extraction, incubation and detection. Microfluidics, which
made these achievements possible, represents a multidisciplinary field that builds on the
advances in physics, chemistry, biotechnology and engineering with the aim to design and
develop systems for fully automated operations with very small volumes of liquids in microsized channels with typical dimension of 1-100 µm.