Toxicologist skills for your resume and career

In the next 3-5 years, the following skills will be more important and prevalent in the field of
biotechnology:
1. AI and Machine Learning: Proficiency in artificial intelligence (AI) will be crucial for
analyzing large biological datasets, identifying patterns, and predicting outcomes in
biotechnology research. Skills in developing AI algorithms tailored for biotechnological
applications, such as drug discovery, genetic engineering, and personalized medicine, will
be in high demand.
2. Data Science and Bioinformatics: Proficiency in bioinformatics tools and algorithms for
sequence analysis, structural biology, and systems biology will be essential to interpret
complex biological datasets to accelerate biotechnology research.
3. Synthetic Biology: Competence in genetic engineering, DNA synthesis, and pathway
optimization is crucial for advancing the biotechnology field and designing genetically
engineered organisms applicable in medicine, agriculture, and industrial biotechnology
4. Tissue Engineering: Expertise in tissue engineering techniques, including biomaterials
design, scaffold fabrication, and cell culture methodologies, will be essential for creating
complex biological tissues and organs for therapeutic applications. Skills in 3D bioprinting
and organ-on-a-chip platforms will enable the development of functional tissue substitutes
for regenerative medicine and drug testing.
5. Stem Cell Therapy: Proficiency in stem cell biology, including stem cell isolation,
characterization, and differentiation protocols, will be critical to advance stem cell-based
therapies. Skills in gene editing technologies, such as CRISPR-Cas9, for precise
modification of stem cells, will enhance their therapeutic potential. Additionally, expertise
in regulatory requirements and quality control standards for stem cell-based products will
be essential for navigating the regulatory landscape and translating stem cell therapies to
the clinic.

The skill sets that young graduates will need when they graduate and enter the workforce are similar to and vastly different from just 15-30 years ago. If they are working in a laboratory setting, then the standards are the same; accurate pipetting, the ability to make complex buffers, and understanding how all the necessary equipment in a lab works. However, that is not nearly enough nowadays. The equipment and instrumentation have been expanding exponentially to the point that you will be working with both expensive and complicated instruments to generate a more considerable amount of data than anyone ever thought possible. Standards for labs today will be using digital imaging devices to capture everything from microscopic images, to western blots, to automated living cell analysis using multi-well plates. Multiplexed assays for 27 to 50 to 1050 cytokines and proteins have replaced single marker ELISA. But knowing ELISA will allow you to be trained to do the multiplexed assays. Most pharmaceutical companies have a great need still for 'old-fashioned' HPLC techniques. Every student I have had in my research techniques class, that graduates and goes for a Pharma position, comes back and tells me they asked them if they could run an HPLC.
Some were even given a test to see if they understood the concept. This then leads to mass spectrometry, LCMS, MALDI-TOF, and even GCMS, and everything that has been developed around those basic techniques is now commonplace in most core facilities and Pharma. New methods for flow cytometry, FACS, are necessary for the higher throughput drug discovery types of labs. Molecular biology has evolved from simple PCR machines that could run 24 samples, just 25 years ago, to digital PCR machines that can run 384 pieces today and email the final data to you at home, while you sleep. Knowing how to calculate the PCR data is extremely critical, as it isn't intuitive, and people tend to take short cuts. Knowing how to do that will be vital. Cell culture and working with animals are still common ways to generate data in any lab, and people who have those skills will always have a job. What do all these techniques have in common? They all have evolved to the point that no one is an expert in every one of them. Labs focus and concentrate on the ones they need the most and make use of them over a long period. What a student should develop is what I call a big toolbox. Learn as many of these techniques as you can, and then use them. Understanding that these are all cyclic and that you may get rusty, or the technology will change. It doesn't matter. By being trained in any of these, it will mean that you can be prepared for other things, that you can catch up and learn and update your techniques in your toolbox. This is what any PI running a lab will be looking for, someone who can be trained, and can evolve and adapt to different technologies, know how they work and how they can be used, what the data looks like when it is working well, and what it looks like when it isn't. The people who have these skills will always be employable.

There is a greater need than ever for workers to analyze data and synthesize a reasonable idea about what it means. This means that they must understand their experiments at a deeper level than just pipetting buffers and timing reactions. They must know what is happening, and if there is a problem, first, they have a problem and then how to solve it. Bioinformatics has become one of the fastest-growing fields. The increased amount of data, whether from standard assays run in an ordinary lab or high throughput data, needs more crunching. The future researcher will not be able to get by just knowing how to use a computer stats program but will be required to understand how to run data in R or Python or whatever new data analysis package is coming next. This becomes even more critical as the data becomes more complex, i.e., 27 cytokines analyzed in 3 different tissues over three other times, from 14 different groups, 6 of which are controls, with the rest being toxin and then treatment groups and authorities. A simple two way ANOVA just doesn't cut it. For this, machine learning tools, pattern recognition, neural networks, topological data analysis (TDA), Deep Learning, etc., are becoming the norm and are being advanced and changed to give more and more substance to what the data means. Students who can operate instruments to generate data and run more complex types of analysis on this 'big data' are in great demand. Likewise, learning the computer-generated design of drugs 'in silico' is a growing field that is now required to screen tens of thousands of compounds before generating them in the lab. This will need someone who can think three-dimensionally; even though the software and advanced computers can do that, it helps if your brain is wired that way, at least a little.

Aside from instruments and complex data analysis, consider where the clinical research is headed. With COVID19, the need to quickly advance drugs from potential use to clinical application has undergone an exponential increase. Lives are being lost daily to the lack of a vaccine or medication that can attenuate to any level the impact the virus has on the human body. The future clinical researcher will need to understand how the instruments work and how tests are run, how a vaccine works, how the virus or disease manifests itself, and how to get it under control. This will only be possible if the researcher is familiar with much of what I wrote above. You won't need to be an expert on virtually everything, but you'll need to understand it so you can use it to synthesize new ideas that may be applicable in the clinical environment. COVID19 is a perfect example. One of the early struggles with this virus was how to test for it. Antibodies weren't developed for it in the very beginning, so an ELISA was out.

In contrast, PCR is one of the most sensitive methods to identify genetic material, such as viruses. So, early on, PCR primers were created that could be used to run a PCR to determine if a person had a live virus. However, the first such PCRs had high false negatives and positives. Further refinement led to the creation of PCR primer sets and protocols that allowed for a more accurate and faster test. An advantage that anyone who has been trained in biotechnology will know the basics of developing a test. If it is a PCR, then what goes into that. Suppose it is an ELISA, how it works, and what you need to set it up. Imagine a test strip similar to the one used for at-home pregnancy tests. This came about in much the same way, through experimentation and developing a way to lower the false negatives and positives, to allow a quick, 5-minute test that could determine if a particular hormone was in your urine at a stage of pregnancy when many women may not have realized there was a possibility they could be pregnant. The person entering the workforce that can think in these ways will be employable and will be able to move between jobs and continue with a very successful and enriching career.

The soft skills all graduates should possess in order to be successful are communication, teamwork, and leadership skills. Specially, they need to be able to explain their research and communicate about their project with non-technical people rather than just other scientists. Furthermore, although academic research has become more collaborative over the past decade, graduate students typically have ownership of their thesis project whereas in industry they will need to be working with many others on a team in order to move a product from bench to bedside. Finally, it is important to be able to inspire and motivate others to work toward a common goal in industry so leadership qualities make an individual stand out.

Two that come to the top of mind, both of which have been amplified by the pandemic. First, a necessary skill is the ability to competently use various instructional platforms and technology-based engagement tools. When online education is prevalent and remote workforces are growing, innovative and contemporary technologies are essential for both pedagogical and student career success. Second, in the current climate of higher education, having demonstrated strategies and skills for student recruitment and yield is critical and highly valued. Recruitment is everyone's responsibility, and big ideas can equal big opportunities for you, even as an instructor.